Yufei
3 years ago
1489 changed files with 245068 additions and 7 deletions
@ -0,0 +1,8 @@ |
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cff-version: 1.2.0 |
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message: "If you use this software, please cite it as below." |
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authors: |
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- name: "MMPose Contributors" |
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title: "OpenMMLab Pose Estimation Toolbox and Benchmark" |
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date-released: 2020-08-31 |
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url: "https://github.com/open-mmlab/mmpose" |
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license: Apache-2.0 |
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@ -0,0 +1,203 @@ |
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Copyright 2018-2020 Open-MMLab. All rights reserved. |
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Apache License |
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Copyright 2018-2020 Open-MMLab. |
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@ -0,0 +1,5 @@ |
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include requirements/*.txt |
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include mmpose/.mim/model-index.yml |
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recursive-include mmpose/.mim/configs *.py *.yml |
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recursive-include mmpose/.mim/tools *.py *.sh |
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recursive-include mmpose/.mim/demo *.py |
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@ -0,0 +1,384 @@ |
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dataset_info = dict( |
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dataset_name='300w', |
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paper_info=dict( |
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author='Sagonas, Christos and Antonakos, Epameinondas ' |
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'and Tzimiropoulos, Georgios and Zafeiriou, Stefanos ' |
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'and Pantic, Maja', |
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title='300 faces in-the-wild challenge: ' |
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'Database and results', |
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container='Image and vision computing', |
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year='2016', |
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homepage='https://ibug.doc.ic.ac.uk/resources/300-W/', |
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), |
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keypoint_info={ |
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0: |
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dict( |
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name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-16'), |
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1: |
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dict( |
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name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-15'), |
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2: |
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dict( |
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name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-14'), |
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3: |
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dict( |
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name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-13'), |
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4: |
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dict( |
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name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-12'), |
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5: |
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dict( |
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name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-11'), |
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6: |
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dict( |
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name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-10'), |
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7: |
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dict(name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-9'), |
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8: |
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dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap=''), |
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9: |
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dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-7'), |
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10: |
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dict( |
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name='kpt-10', id=10, color=[255, 255, 255], type='', |
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swap='kpt-6'), |
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11: |
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dict( |
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name='kpt-11', id=11, color=[255, 255, 255], type='', |
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swap='kpt-5'), |
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12: |
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dict( |
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name='kpt-12', id=12, color=[255, 255, 255], type='', |
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swap='kpt-4'), |
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13: |
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dict( |
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name='kpt-13', id=13, color=[255, 255, 255], type='', |
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swap='kpt-3'), |
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14: |
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dict( |
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name='kpt-14', id=14, color=[255, 255, 255], type='', |
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swap='kpt-2'), |
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15: |
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dict( |
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name='kpt-15', id=15, color=[255, 255, 255], type='', |
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swap='kpt-1'), |
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16: |
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dict( |
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name='kpt-16', id=16, color=[255, 255, 255], type='', |
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swap='kpt-0'), |
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17: |
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dict( |
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name='kpt-17', |
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id=17, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-26'), |
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18: |
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dict( |
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name='kpt-18', |
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id=18, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-25'), |
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19: |
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dict( |
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name='kpt-19', |
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id=19, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-24'), |
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20: |
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dict( |
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name='kpt-20', |
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id=20, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-23'), |
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21: |
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dict( |
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name='kpt-21', |
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id=21, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-22'), |
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22: |
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dict( |
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name='kpt-22', |
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id=22, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-21'), |
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23: |
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dict( |
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name='kpt-23', |
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id=23, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-20'), |
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24: |
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dict( |
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name='kpt-24', |
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id=24, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-19'), |
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25: |
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dict( |
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name='kpt-25', |
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id=25, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-18'), |
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26: |
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dict( |
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name='kpt-26', |
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id=26, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-17'), |
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27: |
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dict(name='kpt-27', id=27, color=[255, 255, 255], type='', swap=''), |
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28: |
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dict(name='kpt-28', id=28, color=[255, 255, 255], type='', swap=''), |
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29: |
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dict(name='kpt-29', id=29, color=[255, 255, 255], type='', swap=''), |
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30: |
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dict(name='kpt-30', id=30, color=[255, 255, 255], type='', swap=''), |
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31: |
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dict( |
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name='kpt-31', |
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id=31, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-35'), |
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32: |
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dict( |
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name='kpt-32', |
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id=32, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-34'), |
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33: |
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dict(name='kpt-33', id=33, color=[255, 255, 255], type='', swap=''), |
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34: |
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dict( |
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name='kpt-34', |
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id=34, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-32'), |
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35: |
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dict( |
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name='kpt-35', |
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id=35, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-31'), |
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36: |
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dict( |
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name='kpt-36', |
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id=36, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-45'), |
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37: |
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dict( |
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name='kpt-37', |
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id=37, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-44'), |
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38: |
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dict( |
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name='kpt-38', |
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id=38, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-43'), |
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39: |
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dict( |
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name='kpt-39', |
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id=39, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-42'), |
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40: |
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dict( |
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name='kpt-40', |
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id=40, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-47'), |
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41: |
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dict( |
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name='kpt-41', |
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id=41, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-46'), |
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42: |
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dict( |
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name='kpt-42', |
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id=42, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-39'), |
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43: |
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dict( |
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name='kpt-43', |
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id=43, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-38'), |
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44: |
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dict( |
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name='kpt-44', |
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id=44, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-37'), |
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45: |
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dict( |
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name='kpt-45', |
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id=45, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-36'), |
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46: |
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dict( |
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name='kpt-46', |
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id=46, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-41'), |
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47: |
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dict( |
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name='kpt-47', |
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id=47, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-40'), |
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48: |
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dict( |
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name='kpt-48', |
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id=48, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-54'), |
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49: |
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dict( |
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name='kpt-49', |
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id=49, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-53'), |
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50: |
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dict( |
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name='kpt-50', |
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id=50, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-52'), |
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51: |
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dict(name='kpt-51', id=51, color=[255, 255, 255], type='', swap=''), |
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52: |
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dict( |
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name='kpt-52', |
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id=52, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-50'), |
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53: |
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dict( |
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name='kpt-53', |
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id=53, |
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color=[255, 255, 255], |
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type='', |
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swap='kpt-49'), |
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54: |
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dict( |
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name='kpt-54', |
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id=54, |
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color=[255, 255, 255], |
|||
type='', |
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swap='kpt-48'), |
|||
55: |
|||
dict( |
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name='kpt-55', |
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id=55, |
|||
color=[255, 255, 255], |
|||
type='', |
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swap='kpt-59'), |
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56: |
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dict( |
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name='kpt-56', |
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id=56, |
|||
color=[255, 255, 255], |
|||
type='', |
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swap='kpt-58'), |
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57: |
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dict(name='kpt-57', id=57, color=[255, 255, 255], type='', swap=''), |
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58: |
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dict( |
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name='kpt-58', |
|||
id=58, |
|||
color=[255, 255, 255], |
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type='', |
|||
swap='kpt-56'), |
|||
59: |
|||
dict( |
|||
name='kpt-59', |
|||
id=59, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-55'), |
|||
60: |
|||
dict( |
|||
name='kpt-60', |
|||
id=60, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-64'), |
|||
61: |
|||
dict( |
|||
name='kpt-61', |
|||
id=61, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-63'), |
|||
62: |
|||
dict(name='kpt-62', id=62, color=[255, 255, 255], type='', swap=''), |
|||
63: |
|||
dict( |
|||
name='kpt-63', |
|||
id=63, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-61'), |
|||
64: |
|||
dict( |
|||
name='kpt-64', |
|||
id=64, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-60'), |
|||
65: |
|||
dict( |
|||
name='kpt-65', |
|||
id=65, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-67'), |
|||
66: |
|||
dict(name='kpt-66', id=66, color=[255, 255, 255], type='', swap=''), |
|||
67: |
|||
dict( |
|||
name='kpt-67', |
|||
id=67, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-65'), |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 68, |
|||
sigmas=[]) |
|||
@ -0,0 +1,83 @@ |
|||
dataset_info = dict( |
|||
dataset_name='aflw', |
|||
paper_info=dict( |
|||
author='Koestinger, Martin and Wohlhart, Paul and ' |
|||
'Roth, Peter M and Bischof, Horst', |
|||
title='Annotated facial landmarks in the wild: ' |
|||
'A large-scale, real-world database for facial ' |
|||
'landmark localization', |
|||
container='2011 IEEE international conference on computer ' |
|||
'vision workshops (ICCV workshops)', |
|||
year='2011', |
|||
homepage='https://www.tugraz.at/institute/icg/research/' |
|||
'team-bischof/lrs/downloads/aflw/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-5'), |
|||
1: |
|||
dict(name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-4'), |
|||
2: |
|||
dict(name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-3'), |
|||
3: |
|||
dict(name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-2'), |
|||
4: |
|||
dict(name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-1'), |
|||
5: |
|||
dict(name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-0'), |
|||
6: |
|||
dict( |
|||
name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-11'), |
|||
7: |
|||
dict( |
|||
name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-10'), |
|||
8: |
|||
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-9'), |
|||
9: |
|||
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-8'), |
|||
10: |
|||
dict( |
|||
name='kpt-10', id=10, color=[255, 255, 255], type='', |
|||
swap='kpt-7'), |
|||
11: |
|||
dict( |
|||
name='kpt-11', id=11, color=[255, 255, 255], type='', |
|||
swap='kpt-6'), |
|||
12: |
|||
dict( |
|||
name='kpt-12', |
|||
id=12, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-14'), |
|||
13: |
|||
dict(name='kpt-13', id=13, color=[255, 255, 255], type='', swap=''), |
|||
14: |
|||
dict( |
|||
name='kpt-14', |
|||
id=14, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-12'), |
|||
15: |
|||
dict( |
|||
name='kpt-15', |
|||
id=15, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-17'), |
|||
16: |
|||
dict(name='kpt-16', id=16, color=[255, 255, 255], type='', swap=''), |
|||
17: |
|||
dict( |
|||
name='kpt-17', |
|||
id=17, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-15'), |
|||
18: |
|||
dict(name='kpt-18', id=18, color=[255, 255, 255], type='', swap='') |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 19, |
|||
sigmas=[]) |
|||
@ -0,0 +1,140 @@ |
|||
dataset_info = dict( |
|||
dataset_name='aic', |
|||
paper_info=dict( |
|||
author='Wu, Jiahong and Zheng, He and Zhao, Bo and ' |
|||
'Li, Yixin and Yan, Baoming and Liang, Rui and ' |
|||
'Wang, Wenjia and Zhou, Shipei and Lin, Guosen and ' |
|||
'Fu, Yanwei and others', |
|||
title='Ai challenger: A large-scale dataset for going ' |
|||
'deeper in image understanding', |
|||
container='arXiv', |
|||
year='2017', |
|||
homepage='https://github.com/AIChallenger/AI_Challenger_2017', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=0, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
1: |
|||
dict( |
|||
name='right_elbow', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
2: |
|||
dict( |
|||
name='right_wrist', |
|||
id=2, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
3: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=3, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
4: |
|||
dict( |
|||
name='left_elbow', |
|||
id=4, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
5: |
|||
dict( |
|||
name='left_wrist', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
6: |
|||
dict( |
|||
name='right_hip', |
|||
id=6, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
7: |
|||
dict( |
|||
name='right_knee', |
|||
id=7, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
8: |
|||
dict( |
|||
name='right_ankle', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
9: |
|||
dict( |
|||
name='left_hip', |
|||
id=9, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
10: |
|||
dict( |
|||
name='left_knee', |
|||
id=10, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
11: |
|||
dict( |
|||
name='left_ankle', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
12: |
|||
dict( |
|||
name='head_top', |
|||
id=12, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap=''), |
|||
13: |
|||
dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('right_wrist', 'right_elbow'), id=0, color=[255, 128, 0]), |
|||
1: dict( |
|||
link=('right_elbow', 'right_shoulder'), id=1, color=[255, 128, 0]), |
|||
2: dict(link=('right_shoulder', 'neck'), id=2, color=[51, 153, 255]), |
|||
3: dict(link=('neck', 'left_shoulder'), id=3, color=[51, 153, 255]), |
|||
4: dict(link=('left_shoulder', 'left_elbow'), id=4, color=[0, 255, 0]), |
|||
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]), |
|||
6: dict(link=('right_ankle', 'right_knee'), id=6, color=[255, 128, 0]), |
|||
7: dict(link=('right_knee', 'right_hip'), id=7, color=[255, 128, 0]), |
|||
8: dict(link=('right_hip', 'left_hip'), id=8, color=[51, 153, 255]), |
|||
9: dict(link=('left_hip', 'left_knee'), id=9, color=[0, 255, 0]), |
|||
10: dict(link=('left_knee', 'left_ankle'), id=10, color=[0, 255, 0]), |
|||
11: dict(link=('head_top', 'neck'), id=11, color=[51, 153, 255]), |
|||
12: dict( |
|||
link=('right_shoulder', 'right_hip'), id=12, color=[51, 153, 255]), |
|||
13: |
|||
dict(link=('left_shoulder', 'left_hip'), id=13, color=[51, 153, 255]) |
|||
}, |
|||
joint_weights=[ |
|||
1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1. |
|||
], |
|||
|
|||
# 'https://github.com/AIChallenger/AI_Challenger_2017/blob/master/' |
|||
# 'Evaluation/keypoint_eval/keypoint_eval.py#L50' |
|||
# delta = 2 x sigma |
|||
sigmas=[ |
|||
0.01388152, 0.01515228, 0.01057665, 0.01417709, 0.01497891, 0.01402144, |
|||
0.03909642, 0.03686941, 0.01981803, 0.03843971, 0.03412318, 0.02415081, |
|||
0.01291456, 0.01236173 |
|||
]) |
|||
@ -0,0 +1,166 @@ |
|||
dataset_info = dict( |
|||
dataset_name='animalpose', |
|||
paper_info=dict( |
|||
author='Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and ' |
|||
'Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing', |
|||
title='Cross-Domain Adaptation for Animal Pose Estimation', |
|||
container='The IEEE International Conference on ' |
|||
'Computer Vision (ICCV)', |
|||
year='2019', |
|||
homepage='https://sites.google.com/view/animal-pose/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'), |
|||
1: |
|||
dict( |
|||
name='R_Eye', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='L_Eye'), |
|||
2: |
|||
dict( |
|||
name='L_EarBase', |
|||
id=2, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='R_EarBase'), |
|||
3: |
|||
dict( |
|||
name='R_EarBase', |
|||
id=3, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='L_EarBase'), |
|||
4: |
|||
dict(name='Nose', id=4, color=[51, 153, 255], type='upper', swap=''), |
|||
5: |
|||
dict(name='Throat', id=5, color=[51, 153, 255], type='upper', swap=''), |
|||
6: |
|||
dict( |
|||
name='TailBase', id=6, color=[51, 153, 255], type='lower', |
|||
swap=''), |
|||
7: |
|||
dict( |
|||
name='Withers', id=7, color=[51, 153, 255], type='upper', swap=''), |
|||
8: |
|||
dict( |
|||
name='L_F_Elbow', |
|||
id=8, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='R_F_Elbow'), |
|||
9: |
|||
dict( |
|||
name='R_F_Elbow', |
|||
id=9, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='L_F_Elbow'), |
|||
10: |
|||
dict( |
|||
name='L_B_Elbow', |
|||
id=10, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='R_B_Elbow'), |
|||
11: |
|||
dict( |
|||
name='R_B_Elbow', |
|||
id=11, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='L_B_Elbow'), |
|||
12: |
|||
dict( |
|||
name='L_F_Knee', |
|||
id=12, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='R_F_Knee'), |
|||
13: |
|||
dict( |
|||
name='R_F_Knee', |
|||
id=13, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='L_F_Knee'), |
|||
14: |
|||
dict( |
|||
name='L_B_Knee', |
|||
id=14, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='R_B_Knee'), |
|||
15: |
|||
dict( |
|||
name='R_B_Knee', |
|||
id=15, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='L_B_Knee'), |
|||
16: |
|||
dict( |
|||
name='L_F_Paw', |
|||
id=16, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='R_F_Paw'), |
|||
17: |
|||
dict( |
|||
name='R_F_Paw', |
|||
id=17, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='L_F_Paw'), |
|||
18: |
|||
dict( |
|||
name='L_B_Paw', |
|||
id=18, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='R_B_Paw'), |
|||
19: |
|||
dict( |
|||
name='R_B_Paw', |
|||
id=19, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='L_B_Paw') |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[51, 153, 255]), |
|||
1: dict(link=('L_Eye', 'L_EarBase'), id=1, color=[0, 255, 0]), |
|||
2: dict(link=('R_Eye', 'R_EarBase'), id=2, color=[255, 128, 0]), |
|||
3: dict(link=('L_Eye', 'Nose'), id=3, color=[0, 255, 0]), |
|||
4: dict(link=('R_Eye', 'Nose'), id=4, color=[255, 128, 0]), |
|||
5: dict(link=('Nose', 'Throat'), id=5, color=[51, 153, 255]), |
|||
6: dict(link=('Throat', 'Withers'), id=6, color=[51, 153, 255]), |
|||
7: dict(link=('TailBase', 'Withers'), id=7, color=[51, 153, 255]), |
|||
8: dict(link=('Throat', 'L_F_Elbow'), id=8, color=[0, 255, 0]), |
|||
9: dict(link=('L_F_Elbow', 'L_F_Knee'), id=9, color=[0, 255, 0]), |
|||
10: dict(link=('L_F_Knee', 'L_F_Paw'), id=10, color=[0, 255, 0]), |
|||
11: dict(link=('Throat', 'R_F_Elbow'), id=11, color=[255, 128, 0]), |
|||
12: dict(link=('R_F_Elbow', 'R_F_Knee'), id=12, color=[255, 128, 0]), |
|||
13: dict(link=('R_F_Knee', 'R_F_Paw'), id=13, color=[255, 128, 0]), |
|||
14: dict(link=('TailBase', 'L_B_Elbow'), id=14, color=[0, 255, 0]), |
|||
15: dict(link=('L_B_Elbow', 'L_B_Knee'), id=15, color=[0, 255, 0]), |
|||
16: dict(link=('L_B_Knee', 'L_B_Paw'), id=16, color=[0, 255, 0]), |
|||
17: dict(link=('TailBase', 'R_B_Elbow'), id=17, color=[255, 128, 0]), |
|||
18: dict(link=('R_B_Elbow', 'R_B_Knee'), id=18, color=[255, 128, 0]), |
|||
19: dict(link=('R_B_Knee', 'R_B_Paw'), id=19, color=[255, 128, 0]) |
|||
}, |
|||
joint_weights=[ |
|||
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.2, 1.2, |
|||
1.5, 1.5, 1.5, 1.5 |
|||
], |
|||
|
|||
# Note: The original paper did not provide enough information about |
|||
# the sigmas. We modified from 'https://github.com/cocodataset/' |
|||
# 'cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py#L523' |
|||
sigmas=[ |
|||
0.025, 0.025, 0.026, 0.035, 0.035, 0.10, 0.10, 0.10, 0.107, 0.107, |
|||
0.107, 0.107, 0.087, 0.087, 0.087, 0.087, 0.089, 0.089, 0.089, 0.089 |
|||
]) |
|||
@ -0,0 +1,142 @@ |
|||
dataset_info = dict( |
|||
dataset_name='ap10k', |
|||
paper_info=dict( |
|||
author='Yu, Hang and Xu, Yufei and Zhang, Jing and ' |
|||
'Zhao, Wei and Guan, Ziyu and Tao, Dacheng', |
|||
title='AP-10K: A Benchmark for Animal Pose Estimation in the Wild', |
|||
container='35th Conference on Neural Information Processing Systems ' |
|||
'(NeurIPS 2021) Track on Datasets and Bench-marks.', |
|||
year='2021', |
|||
homepage='https://github.com/AlexTheBad/AP-10K', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'), |
|||
1: |
|||
dict( |
|||
name='R_Eye', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='L_Eye'), |
|||
2: |
|||
dict(name='Nose', id=2, color=[51, 153, 255], type='upper', swap=''), |
|||
3: |
|||
dict(name='Neck', id=3, color=[51, 153, 255], type='upper', swap=''), |
|||
4: |
|||
dict( |
|||
name='Root of tail', |
|||
id=4, |
|||
color=[51, 153, 255], |
|||
type='lower', |
|||
swap=''), |
|||
5: |
|||
dict( |
|||
name='L_Shoulder', |
|||
id=5, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='R_Shoulder'), |
|||
6: |
|||
dict( |
|||
name='L_Elbow', |
|||
id=6, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='R_Elbow'), |
|||
7: |
|||
dict( |
|||
name='L_F_Paw', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='R_F_Paw'), |
|||
8: |
|||
dict( |
|||
name='R_Shoulder', |
|||
id=8, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='L_Shoulder'), |
|||
9: |
|||
dict( |
|||
name='R_Elbow', |
|||
id=9, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='L_Elbow'), |
|||
10: |
|||
dict( |
|||
name='R_F_Paw', |
|||
id=10, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='L_F_Paw'), |
|||
11: |
|||
dict( |
|||
name='L_Hip', |
|||
id=11, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='R_Hip'), |
|||
12: |
|||
dict( |
|||
name='L_Knee', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='R_Knee'), |
|||
13: |
|||
dict( |
|||
name='L_B_Paw', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='R_B_Paw'), |
|||
14: |
|||
dict( |
|||
name='R_Hip', id=14, color=[0, 255, 0], type='lower', |
|||
swap='L_Hip'), |
|||
15: |
|||
dict( |
|||
name='R_Knee', |
|||
id=15, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='L_Knee'), |
|||
16: |
|||
dict( |
|||
name='R_B_Paw', |
|||
id=16, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='L_B_Paw'), |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[0, 0, 255]), |
|||
1: dict(link=('L_Eye', 'Nose'), id=1, color=[0, 0, 255]), |
|||
2: dict(link=('R_Eye', 'Nose'), id=2, color=[0, 0, 255]), |
|||
3: dict(link=('Nose', 'Neck'), id=3, color=[0, 255, 0]), |
|||
4: dict(link=('Neck', 'Root of tail'), id=4, color=[0, 255, 0]), |
|||
5: dict(link=('Neck', 'L_Shoulder'), id=5, color=[0, 255, 255]), |
|||
6: dict(link=('L_Shoulder', 'L_Elbow'), id=6, color=[0, 255, 255]), |
|||
7: dict(link=('L_Elbow', 'L_F_Paw'), id=6, color=[0, 255, 255]), |
|||
8: dict(link=('Neck', 'R_Shoulder'), id=7, color=[6, 156, 250]), |
|||
9: dict(link=('R_Shoulder', 'R_Elbow'), id=8, color=[6, 156, 250]), |
|||
10: dict(link=('R_Elbow', 'R_F_Paw'), id=9, color=[6, 156, 250]), |
|||
11: dict(link=('Root of tail', 'L_Hip'), id=10, color=[0, 255, 255]), |
|||
12: dict(link=('L_Hip', 'L_Knee'), id=11, color=[0, 255, 255]), |
|||
13: dict(link=('L_Knee', 'L_B_Paw'), id=12, color=[0, 255, 255]), |
|||
14: dict(link=('Root of tail', 'R_Hip'), id=13, color=[6, 156, 250]), |
|||
15: dict(link=('R_Hip', 'R_Knee'), id=14, color=[6, 156, 250]), |
|||
16: dict(link=('R_Knee', 'R_B_Paw'), id=15, color=[6, 156, 250]), |
|||
}, |
|||
joint_weights=[ |
|||
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, |
|||
1.5 |
|||
], |
|||
sigmas=[ |
|||
0.025, 0.025, 0.026, 0.035, 0.035, 0.079, 0.072, 0.062, 0.079, 0.072, |
|||
0.062, 0.107, 0.087, 0.089, 0.107, 0.087, 0.089 |
|||
]) |
|||
@ -0,0 +1,144 @@ |
|||
dataset_info = dict( |
|||
dataset_name='atrw', |
|||
paper_info=dict( |
|||
author='Li, Shuyuan and Li, Jianguo and Tang, Hanlin ' |
|||
'and Qian, Rui and Lin, Weiyao', |
|||
title='ATRW: A Benchmark for Amur Tiger ' |
|||
'Re-identification in the Wild', |
|||
container='Proceedings of the 28th ACM ' |
|||
'International Conference on Multimedia', |
|||
year='2020', |
|||
homepage='https://cvwc2019.github.io/challenge.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='left_ear', |
|||
id=0, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_ear'), |
|||
1: |
|||
dict( |
|||
name='right_ear', |
|||
id=1, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_ear'), |
|||
2: |
|||
dict(name='nose', id=2, color=[51, 153, 255], type='upper', swap=''), |
|||
3: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=3, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
4: |
|||
dict( |
|||
name='right_front_paw', |
|||
id=4, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_front_paw'), |
|||
5: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
6: |
|||
dict( |
|||
name='left_front_paw', |
|||
id=6, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_front_paw'), |
|||
7: |
|||
dict( |
|||
name='right_hip', |
|||
id=7, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
8: |
|||
dict( |
|||
name='right_knee', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
9: |
|||
dict( |
|||
name='right_back_paw', |
|||
id=9, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_back_paw'), |
|||
10: |
|||
dict( |
|||
name='left_hip', |
|||
id=10, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
11: |
|||
dict( |
|||
name='left_knee', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
12: |
|||
dict( |
|||
name='left_back_paw', |
|||
id=12, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_back_paw'), |
|||
13: |
|||
dict(name='tail', id=13, color=[51, 153, 255], type='lower', swap=''), |
|||
14: |
|||
dict( |
|||
name='center', id=14, color=[51, 153, 255], type='lower', swap=''), |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('left_ear', 'nose'), id=0, color=[51, 153, 255]), |
|||
1: |
|||
dict(link=('right_ear', 'nose'), id=1, color=[51, 153, 255]), |
|||
2: |
|||
dict(link=('nose', 'center'), id=2, color=[51, 153, 255]), |
|||
3: |
|||
dict( |
|||
link=('left_shoulder', 'left_front_paw'), id=3, color=[0, 255, 0]), |
|||
4: |
|||
dict(link=('left_shoulder', 'center'), id=4, color=[0, 255, 0]), |
|||
5: |
|||
dict( |
|||
link=('right_shoulder', 'right_front_paw'), |
|||
id=5, |
|||
color=[255, 128, 0]), |
|||
6: |
|||
dict(link=('right_shoulder', 'center'), id=6, color=[255, 128, 0]), |
|||
7: |
|||
dict(link=('tail', 'center'), id=7, color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('right_back_paw', 'right_knee'), id=8, color=[255, 128, 0]), |
|||
9: |
|||
dict(link=('right_knee', 'right_hip'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict(link=('right_hip', 'tail'), id=10, color=[255, 128, 0]), |
|||
11: |
|||
dict(link=('left_back_paw', 'left_knee'), id=11, color=[0, 255, 0]), |
|||
12: |
|||
dict(link=('left_knee', 'left_hip'), id=12, color=[0, 255, 0]), |
|||
13: |
|||
dict(link=('left_hip', 'tail'), id=13, color=[0, 255, 0]), |
|||
}, |
|||
joint_weights=[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.], |
|||
sigmas=[ |
|||
0.0277, 0.0823, 0.0831, 0.0202, 0.0716, 0.0263, 0.0646, 0.0302, 0.0440, |
|||
0.0316, 0.0333, 0.0547, 0.0263, 0.0683, 0.0539 |
|||
]) |
|||
@ -0,0 +1,181 @@ |
|||
dataset_info = dict( |
|||
dataset_name='coco', |
|||
paper_info=dict( |
|||
author='Lin, Tsung-Yi and Maire, Michael and ' |
|||
'Belongie, Serge and Hays, James and ' |
|||
'Perona, Pietro and Ramanan, Deva and ' |
|||
r'Doll{\'a}r, Piotr and Zitnick, C Lawrence', |
|||
title='Microsoft coco: Common objects in context', |
|||
container='European conference on computer vision', |
|||
year='2014', |
|||
homepage='http://cocodataset.org/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''), |
|||
1: |
|||
dict( |
|||
name='left_eye', |
|||
id=1, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_eye'), |
|||
2: |
|||
dict( |
|||
name='right_eye', |
|||
id=2, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_eye'), |
|||
3: |
|||
dict( |
|||
name='left_ear', |
|||
id=3, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_ear'), |
|||
4: |
|||
dict( |
|||
name='right_ear', |
|||
id=4, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_ear'), |
|||
5: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
6: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=6, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
7: |
|||
dict( |
|||
name='left_elbow', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
8: |
|||
dict( |
|||
name='right_elbow', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
9: |
|||
dict( |
|||
name='left_wrist', |
|||
id=9, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
10: |
|||
dict( |
|||
name='right_wrist', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
11: |
|||
dict( |
|||
name='left_hip', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
12: |
|||
dict( |
|||
name='right_hip', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
13: |
|||
dict( |
|||
name='left_knee', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
14: |
|||
dict( |
|||
name='right_knee', |
|||
id=14, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
15: |
|||
dict( |
|||
name='left_ankle', |
|||
id=15, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
16: |
|||
dict( |
|||
name='right_ankle', |
|||
id=16, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]), |
|||
1: |
|||
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]), |
|||
2: |
|||
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]), |
|||
5: |
|||
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]), |
|||
6: |
|||
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict( |
|||
link=('left_shoulder', 'right_shoulder'), |
|||
id=7, |
|||
color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]), |
|||
9: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]), |
|||
11: |
|||
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]), |
|||
13: |
|||
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]), |
|||
14: |
|||
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]), |
|||
15: |
|||
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]), |
|||
16: |
|||
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]), |
|||
17: |
|||
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]), |
|||
18: |
|||
dict( |
|||
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255]) |
|||
}, |
|||
joint_weights=[ |
|||
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, |
|||
1.5 |
|||
], |
|||
sigmas=[ |
|||
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062, |
|||
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089 |
|||
]) |
|||
File diff suppressed because it is too large
@ -0,0 +1,448 @@ |
|||
dataset_info = dict( |
|||
dataset_name='coco_wholebody_face', |
|||
paper_info=dict( |
|||
author='Jin, Sheng and Xu, Lumin and Xu, Jin and ' |
|||
'Wang, Can and Liu, Wentao and ' |
|||
'Qian, Chen and Ouyang, Wanli and Luo, Ping', |
|||
title='Whole-Body Human Pose Estimation in the Wild', |
|||
container='Proceedings of the European ' |
|||
'Conference on Computer Vision (ECCV)', |
|||
year='2020', |
|||
homepage='https://github.com/jin-s13/COCO-WholeBody/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='face-0', |
|||
id=0, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-16'), |
|||
1: |
|||
dict( |
|||
name='face-1', |
|||
id=1, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-15'), |
|||
2: |
|||
dict( |
|||
name='face-2', |
|||
id=2, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-14'), |
|||
3: |
|||
dict( |
|||
name='face-3', |
|||
id=3, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-13'), |
|||
4: |
|||
dict( |
|||
name='face-4', |
|||
id=4, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-12'), |
|||
5: |
|||
dict( |
|||
name='face-5', |
|||
id=5, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-11'), |
|||
6: |
|||
dict( |
|||
name='face-6', |
|||
id=6, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-10'), |
|||
7: |
|||
dict( |
|||
name='face-7', id=7, color=[255, 255, 255], type='', |
|||
swap='face-9'), |
|||
8: |
|||
dict(name='face-8', id=8, color=[255, 255, 255], type='', swap=''), |
|||
9: |
|||
dict( |
|||
name='face-9', id=9, color=[255, 255, 255], type='', |
|||
swap='face-7'), |
|||
10: |
|||
dict( |
|||
name='face-10', |
|||
id=10, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-6'), |
|||
11: |
|||
dict( |
|||
name='face-11', |
|||
id=11, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-5'), |
|||
12: |
|||
dict( |
|||
name='face-12', |
|||
id=12, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-4'), |
|||
13: |
|||
dict( |
|||
name='face-13', |
|||
id=13, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-3'), |
|||
14: |
|||
dict( |
|||
name='face-14', |
|||
id=14, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-2'), |
|||
15: |
|||
dict( |
|||
name='face-15', |
|||
id=15, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-1'), |
|||
16: |
|||
dict( |
|||
name='face-16', |
|||
id=16, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-0'), |
|||
17: |
|||
dict( |
|||
name='face-17', |
|||
id=17, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-26'), |
|||
18: |
|||
dict( |
|||
name='face-18', |
|||
id=18, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-25'), |
|||
19: |
|||
dict( |
|||
name='face-19', |
|||
id=19, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-24'), |
|||
20: |
|||
dict( |
|||
name='face-20', |
|||
id=20, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-23'), |
|||
21: |
|||
dict( |
|||
name='face-21', |
|||
id=21, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-22'), |
|||
22: |
|||
dict( |
|||
name='face-22', |
|||
id=22, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-21'), |
|||
23: |
|||
dict( |
|||
name='face-23', |
|||
id=23, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-20'), |
|||
24: |
|||
dict( |
|||
name='face-24', |
|||
id=24, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-19'), |
|||
25: |
|||
dict( |
|||
name='face-25', |
|||
id=25, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-18'), |
|||
26: |
|||
dict( |
|||
name='face-26', |
|||
id=26, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-17'), |
|||
27: |
|||
dict(name='face-27', id=27, color=[255, 255, 255], type='', swap=''), |
|||
28: |
|||
dict(name='face-28', id=28, color=[255, 255, 255], type='', swap=''), |
|||
29: |
|||
dict(name='face-29', id=29, color=[255, 255, 255], type='', swap=''), |
|||
30: |
|||
dict(name='face-30', id=30, color=[255, 255, 255], type='', swap=''), |
|||
31: |
|||
dict( |
|||
name='face-31', |
|||
id=31, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-35'), |
|||
32: |
|||
dict( |
|||
name='face-32', |
|||
id=32, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-34'), |
|||
33: |
|||
dict(name='face-33', id=33, color=[255, 255, 255], type='', swap=''), |
|||
34: |
|||
dict( |
|||
name='face-34', |
|||
id=34, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-32'), |
|||
35: |
|||
dict( |
|||
name='face-35', |
|||
id=35, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-31'), |
|||
36: |
|||
dict( |
|||
name='face-36', |
|||
id=36, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-45'), |
|||
37: |
|||
dict( |
|||
name='face-37', |
|||
id=37, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-44'), |
|||
38: |
|||
dict( |
|||
name='face-38', |
|||
id=38, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-43'), |
|||
39: |
|||
dict( |
|||
name='face-39', |
|||
id=39, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-42'), |
|||
40: |
|||
dict( |
|||
name='face-40', |
|||
id=40, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-47'), |
|||
41: |
|||
dict( |
|||
name='face-41', |
|||
id=41, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-46'), |
|||
42: |
|||
dict( |
|||
name='face-42', |
|||
id=42, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-39'), |
|||
43: |
|||
dict( |
|||
name='face-43', |
|||
id=43, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-38'), |
|||
44: |
|||
dict( |
|||
name='face-44', |
|||
id=44, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-37'), |
|||
45: |
|||
dict( |
|||
name='face-45', |
|||
id=45, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-36'), |
|||
46: |
|||
dict( |
|||
name='face-46', |
|||
id=46, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-41'), |
|||
47: |
|||
dict( |
|||
name='face-47', |
|||
id=47, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-40'), |
|||
48: |
|||
dict( |
|||
name='face-48', |
|||
id=48, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-54'), |
|||
49: |
|||
dict( |
|||
name='face-49', |
|||
id=49, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-53'), |
|||
50: |
|||
dict( |
|||
name='face-50', |
|||
id=50, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-52'), |
|||
51: |
|||
dict(name='face-51', id=52, color=[255, 255, 255], type='', swap=''), |
|||
52: |
|||
dict( |
|||
name='face-52', |
|||
id=52, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-50'), |
|||
53: |
|||
dict( |
|||
name='face-53', |
|||
id=53, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-49'), |
|||
54: |
|||
dict( |
|||
name='face-54', |
|||
id=54, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-48'), |
|||
55: |
|||
dict( |
|||
name='face-55', |
|||
id=55, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-59'), |
|||
56: |
|||
dict( |
|||
name='face-56', |
|||
id=56, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-58'), |
|||
57: |
|||
dict(name='face-57', id=57, color=[255, 255, 255], type='', swap=''), |
|||
58: |
|||
dict( |
|||
name='face-58', |
|||
id=58, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-56'), |
|||
59: |
|||
dict( |
|||
name='face-59', |
|||
id=59, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-55'), |
|||
60: |
|||
dict( |
|||
name='face-60', |
|||
id=60, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-64'), |
|||
61: |
|||
dict( |
|||
name='face-61', |
|||
id=61, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-63'), |
|||
62: |
|||
dict(name='face-62', id=62, color=[255, 255, 255], type='', swap=''), |
|||
63: |
|||
dict( |
|||
name='face-63', |
|||
id=63, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-61'), |
|||
64: |
|||
dict( |
|||
name='face-64', |
|||
id=64, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-60'), |
|||
65: |
|||
dict( |
|||
name='face-65', |
|||
id=65, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-67'), |
|||
66: |
|||
dict(name='face-66', id=66, color=[255, 255, 255], type='', swap=''), |
|||
67: |
|||
dict( |
|||
name='face-67', |
|||
id=67, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='face-65') |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 68, |
|||
|
|||
# 'https://github.com/jin-s13/COCO-WholeBody/blob/master/' |
|||
# 'evaluation/myeval_wholebody.py#L177' |
|||
sigmas=[ |
|||
0.042, 0.043, 0.044, 0.043, 0.040, 0.035, 0.031, 0.025, 0.020, 0.023, |
|||
0.029, 0.032, 0.037, 0.038, 0.043, 0.041, 0.045, 0.013, 0.012, 0.011, |
|||
0.011, 0.012, 0.012, 0.011, 0.011, 0.013, 0.015, 0.009, 0.007, 0.007, |
|||
0.007, 0.012, 0.009, 0.008, 0.016, 0.010, 0.017, 0.011, 0.009, 0.011, |
|||
0.009, 0.007, 0.013, 0.008, 0.011, 0.012, 0.010, 0.034, 0.008, 0.008, |
|||
0.009, 0.008, 0.008, 0.007, 0.010, 0.008, 0.009, 0.009, 0.009, 0.007, |
|||
0.007, 0.008, 0.011, 0.008, 0.008, 0.008, 0.01, 0.008 |
|||
]) |
|||
@ -0,0 +1,147 @@ |
|||
dataset_info = dict( |
|||
dataset_name='coco_wholebody_hand', |
|||
paper_info=dict( |
|||
author='Jin, Sheng and Xu, Lumin and Xu, Jin and ' |
|||
'Wang, Can and Liu, Wentao and ' |
|||
'Qian, Chen and Ouyang, Wanli and Luo, Ping', |
|||
title='Whole-Body Human Pose Estimation in the Wild', |
|||
container='Proceedings of the European ' |
|||
'Conference on Computer Vision (ECCV)', |
|||
year='2020', |
|||
homepage='https://github.com/jin-s13/COCO-WholeBody/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''), |
|||
2: |
|||
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''), |
|||
3: |
|||
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''), |
|||
4: |
|||
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''), |
|||
5: |
|||
dict( |
|||
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''), |
|||
6: |
|||
dict( |
|||
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''), |
|||
7: |
|||
dict( |
|||
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''), |
|||
8: |
|||
dict( |
|||
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''), |
|||
9: |
|||
dict( |
|||
name='middle_finger1', |
|||
id=9, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='middle_finger2', |
|||
id=10, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
11: |
|||
dict( |
|||
name='middle_finger3', |
|||
id=11, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
12: |
|||
dict( |
|||
name='middle_finger4', |
|||
id=12, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
13: |
|||
dict( |
|||
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''), |
|||
14: |
|||
dict( |
|||
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''), |
|||
15: |
|||
dict( |
|||
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''), |
|||
16: |
|||
dict( |
|||
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''), |
|||
17: |
|||
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''), |
|||
18: |
|||
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''), |
|||
19: |
|||
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''), |
|||
20: |
|||
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]), |
|||
5: |
|||
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]), |
|||
6: |
|||
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]), |
|||
7: |
|||
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]), |
|||
8: |
|||
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]), |
|||
9: |
|||
dict( |
|||
link=('middle_finger1', 'middle_finger2'), |
|||
id=9, |
|||
color=[102, 178, 255]), |
|||
10: |
|||
dict( |
|||
link=('middle_finger2', 'middle_finger3'), |
|||
id=10, |
|||
color=[102, 178, 255]), |
|||
11: |
|||
dict( |
|||
link=('middle_finger3', 'middle_finger4'), |
|||
id=11, |
|||
color=[102, 178, 255]), |
|||
12: |
|||
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]), |
|||
13: |
|||
dict( |
|||
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]), |
|||
14: |
|||
dict( |
|||
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]), |
|||
15: |
|||
dict( |
|||
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]), |
|||
16: |
|||
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]), |
|||
17: |
|||
dict( |
|||
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]), |
|||
18: |
|||
dict( |
|||
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]), |
|||
19: |
|||
dict( |
|||
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[1.] * 21, |
|||
sigmas=[ |
|||
0.029, 0.022, 0.035, 0.037, 0.047, 0.026, 0.025, 0.024, 0.035, 0.018, |
|||
0.024, 0.022, 0.026, 0.017, 0.021, 0.021, 0.032, 0.02, 0.019, 0.022, |
|||
0.031 |
|||
]) |
|||
@ -0,0 +1,134 @@ |
|||
dataset_info = dict( |
|||
dataset_name='cofw', |
|||
paper_info=dict( |
|||
author='Burgos-Artizzu, Xavier P and Perona, ' |
|||
r'Pietro and Doll{\'a}r, Piotr', |
|||
title='Robust face landmark estimation under occlusion', |
|||
container='Proceedings of the IEEE international ' |
|||
'conference on computer vision', |
|||
year='2013', |
|||
homepage='http://www.vision.caltech.edu/xpburgos/ICCV13/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-1'), |
|||
1: |
|||
dict(name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-0'), |
|||
2: |
|||
dict(name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-3'), |
|||
3: |
|||
dict(name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-2'), |
|||
4: |
|||
dict(name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-6'), |
|||
5: |
|||
dict(name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-7'), |
|||
6: |
|||
dict(name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-4'), |
|||
7: |
|||
dict(name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-5'), |
|||
8: |
|||
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-9'), |
|||
9: |
|||
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-8'), |
|||
10: |
|||
dict( |
|||
name='kpt-10', |
|||
id=10, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-11'), |
|||
11: |
|||
dict( |
|||
name='kpt-11', |
|||
id=11, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-10'), |
|||
12: |
|||
dict( |
|||
name='kpt-12', |
|||
id=12, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-14'), |
|||
13: |
|||
dict( |
|||
name='kpt-13', |
|||
id=13, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-15'), |
|||
14: |
|||
dict( |
|||
name='kpt-14', |
|||
id=14, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-12'), |
|||
15: |
|||
dict( |
|||
name='kpt-15', |
|||
id=15, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-13'), |
|||
16: |
|||
dict( |
|||
name='kpt-16', |
|||
id=16, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-17'), |
|||
17: |
|||
dict( |
|||
name='kpt-17', |
|||
id=17, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-16'), |
|||
18: |
|||
dict( |
|||
name='kpt-18', |
|||
id=18, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-19'), |
|||
19: |
|||
dict( |
|||
name='kpt-19', |
|||
id=19, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-18'), |
|||
20: |
|||
dict(name='kpt-20', id=20, color=[255, 255, 255], type='', swap=''), |
|||
21: |
|||
dict(name='kpt-21', id=21, color=[255, 255, 255], type='', swap=''), |
|||
22: |
|||
dict( |
|||
name='kpt-22', |
|||
id=22, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-23'), |
|||
23: |
|||
dict( |
|||
name='kpt-23', |
|||
id=23, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-22'), |
|||
24: |
|||
dict(name='kpt-24', id=24, color=[255, 255, 255], type='', swap=''), |
|||
25: |
|||
dict(name='kpt-25', id=25, color=[255, 255, 255], type='', swap=''), |
|||
26: |
|||
dict(name='kpt-26', id=26, color=[255, 255, 255], type='', swap=''), |
|||
27: |
|||
dict(name='kpt-27', id=27, color=[255, 255, 255], type='', swap=''), |
|||
28: |
|||
dict(name='kpt-28', id=28, color=[255, 255, 255], type='', swap='') |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 29, |
|||
sigmas=[]) |
|||
@ -0,0 +1,147 @@ |
|||
dataset_info = dict( |
|||
dataset_name='crowdpose', |
|||
paper_info=dict( |
|||
author='Li, Jiefeng and Wang, Can and Zhu, Hao and ' |
|||
'Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu', |
|||
title='CrowdPose: Efficient Crowded Scenes Pose Estimation ' |
|||
'and A New Benchmark', |
|||
container='Proceedings of IEEE Conference on Computer ' |
|||
'Vision and Pattern Recognition (CVPR)', |
|||
year='2019', |
|||
homepage='https://github.com/Jeff-sjtu/CrowdPose', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=0, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
1: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=1, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
2: |
|||
dict( |
|||
name='left_elbow', |
|||
id=2, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
3: |
|||
dict( |
|||
name='right_elbow', |
|||
id=3, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
4: |
|||
dict( |
|||
name='left_wrist', |
|||
id=4, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
5: |
|||
dict( |
|||
name='right_wrist', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
6: |
|||
dict( |
|||
name='left_hip', |
|||
id=6, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
7: |
|||
dict( |
|||
name='right_hip', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
8: |
|||
dict( |
|||
name='left_knee', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
9: |
|||
dict( |
|||
name='right_knee', |
|||
id=9, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
10: |
|||
dict( |
|||
name='left_ankle', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
11: |
|||
dict( |
|||
name='right_ankle', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
12: |
|||
dict( |
|||
name='top_head', id=12, color=[255, 128, 0], type='upper', |
|||
swap=''), |
|||
13: |
|||
dict(name='neck', id=13, color=[0, 255, 0], type='upper', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]), |
|||
1: |
|||
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]), |
|||
2: |
|||
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]), |
|||
5: |
|||
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]), |
|||
6: |
|||
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict( |
|||
link=('left_shoulder', 'right_shoulder'), |
|||
id=7, |
|||
color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]), |
|||
9: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]), |
|||
11: |
|||
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('top_head', 'neck'), id=12, color=[51, 153, 255]), |
|||
13: |
|||
dict(link=('right_shoulder', 'neck'), id=13, color=[51, 153, 255]), |
|||
14: |
|||
dict(link=('left_shoulder', 'neck'), id=14, color=[51, 153, 255]) |
|||
}, |
|||
joint_weights=[ |
|||
0.2, 0.2, 0.2, 1.3, 1.5, 0.2, 1.3, 1.5, 0.2, 0.2, 0.5, 0.2, 0.2, 0.5 |
|||
], |
|||
sigmas=[ |
|||
0.079, 0.079, 0.072, 0.072, 0.062, 0.062, 0.107, 0.107, 0.087, 0.087, |
|||
0.089, 0.089, 0.079, 0.079 |
|||
]) |
|||
@ -0,0 +1,74 @@ |
|||
dataset_info = dict( |
|||
dataset_name='deepfashion_full', |
|||
paper_info=dict( |
|||
author='Liu, Ziwei and Luo, Ping and Qiu, Shi ' |
|||
'and Wang, Xiaogang and Tang, Xiaoou', |
|||
title='DeepFashion: Powering Robust Clothes Recognition ' |
|||
'and Retrieval with Rich Annotations', |
|||
container='Proceedings of IEEE Conference on Computer ' |
|||
'Vision and Pattern Recognition (CVPR)', |
|||
year='2016', |
|||
homepage='http://mmlab.ie.cuhk.edu.hk/projects/' |
|||
'DeepFashion/LandmarkDetection.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='left collar', |
|||
id=0, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right collar'), |
|||
1: |
|||
dict( |
|||
name='right collar', |
|||
id=1, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left collar'), |
|||
2: |
|||
dict( |
|||
name='left sleeve', |
|||
id=2, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right sleeve'), |
|||
3: |
|||
dict( |
|||
name='right sleeve', |
|||
id=3, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left sleeve'), |
|||
4: |
|||
dict( |
|||
name='left waistline', |
|||
id=0, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right waistline'), |
|||
5: |
|||
dict( |
|||
name='right waistline', |
|||
id=1, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left waistline'), |
|||
6: |
|||
dict( |
|||
name='left hem', |
|||
id=2, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right hem'), |
|||
7: |
|||
dict( |
|||
name='right hem', |
|||
id=3, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left hem'), |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 8, |
|||
sigmas=[]) |
|||
@ -0,0 +1,46 @@ |
|||
dataset_info = dict( |
|||
dataset_name='deepfashion_lower', |
|||
paper_info=dict( |
|||
author='Liu, Ziwei and Luo, Ping and Qiu, Shi ' |
|||
'and Wang, Xiaogang and Tang, Xiaoou', |
|||
title='DeepFashion: Powering Robust Clothes Recognition ' |
|||
'and Retrieval with Rich Annotations', |
|||
container='Proceedings of IEEE Conference on Computer ' |
|||
'Vision and Pattern Recognition (CVPR)', |
|||
year='2016', |
|||
homepage='http://mmlab.ie.cuhk.edu.hk/projects/' |
|||
'DeepFashion/LandmarkDetection.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='left waistline', |
|||
id=0, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right waistline'), |
|||
1: |
|||
dict( |
|||
name='right waistline', |
|||
id=1, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left waistline'), |
|||
2: |
|||
dict( |
|||
name='left hem', |
|||
id=2, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right hem'), |
|||
3: |
|||
dict( |
|||
name='right hem', |
|||
id=3, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left hem'), |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 4, |
|||
sigmas=[]) |
|||
@ -0,0 +1,60 @@ |
|||
dataset_info = dict( |
|||
dataset_name='deepfashion_upper', |
|||
paper_info=dict( |
|||
author='Liu, Ziwei and Luo, Ping and Qiu, Shi ' |
|||
'and Wang, Xiaogang and Tang, Xiaoou', |
|||
title='DeepFashion: Powering Robust Clothes Recognition ' |
|||
'and Retrieval with Rich Annotations', |
|||
container='Proceedings of IEEE Conference on Computer ' |
|||
'Vision and Pattern Recognition (CVPR)', |
|||
year='2016', |
|||
homepage='http://mmlab.ie.cuhk.edu.hk/projects/' |
|||
'DeepFashion/LandmarkDetection.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='left collar', |
|||
id=0, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right collar'), |
|||
1: |
|||
dict( |
|||
name='right collar', |
|||
id=1, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left collar'), |
|||
2: |
|||
dict( |
|||
name='left sleeve', |
|||
id=2, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right sleeve'), |
|||
3: |
|||
dict( |
|||
name='right sleeve', |
|||
id=3, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left sleeve'), |
|||
4: |
|||
dict( |
|||
name='left hem', |
|||
id=4, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right hem'), |
|||
5: |
|||
dict( |
|||
name='right hem', |
|||
id=5, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left hem'), |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 6, |
|||
sigmas=[]) |
|||
@ -0,0 +1,237 @@ |
|||
dataset_info = dict( |
|||
dataset_name='fly', |
|||
paper_info=dict( |
|||
author='Pereira, Talmo D and Aldarondo, Diego E and ' |
|||
'Willmore, Lindsay and Kislin, Mikhail and ' |
|||
'Wang, Samuel S-H and Murthy, Mala and Shaevitz, Joshua W', |
|||
title='Fast animal pose estimation using deep neural networks', |
|||
container='Nature methods', |
|||
year='2019', |
|||
homepage='https://github.com/jgraving/DeepPoseKit-Data', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='head', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='eyeL', id=1, color=[255, 255, 255], type='', swap='eyeR'), |
|||
2: |
|||
dict(name='eyeR', id=2, color=[255, 255, 255], type='', swap='eyeL'), |
|||
3: |
|||
dict(name='neck', id=3, color=[255, 255, 255], type='', swap=''), |
|||
4: |
|||
dict(name='thorax', id=4, color=[255, 255, 255], type='', swap=''), |
|||
5: |
|||
dict(name='abdomen', id=5, color=[255, 255, 255], type='', swap=''), |
|||
6: |
|||
dict( |
|||
name='forelegR1', |
|||
id=6, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL1'), |
|||
7: |
|||
dict( |
|||
name='forelegR2', |
|||
id=7, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL2'), |
|||
8: |
|||
dict( |
|||
name='forelegR3', |
|||
id=8, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL3'), |
|||
9: |
|||
dict( |
|||
name='forelegR4', |
|||
id=9, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL4'), |
|||
10: |
|||
dict( |
|||
name='midlegR1', |
|||
id=10, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL1'), |
|||
11: |
|||
dict( |
|||
name='midlegR2', |
|||
id=11, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL2'), |
|||
12: |
|||
dict( |
|||
name='midlegR3', |
|||
id=12, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL3'), |
|||
13: |
|||
dict( |
|||
name='midlegR4', |
|||
id=13, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL4'), |
|||
14: |
|||
dict( |
|||
name='hindlegR1', |
|||
id=14, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL1'), |
|||
15: |
|||
dict( |
|||
name='hindlegR2', |
|||
id=15, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL2'), |
|||
16: |
|||
dict( |
|||
name='hindlegR3', |
|||
id=16, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL3'), |
|||
17: |
|||
dict( |
|||
name='hindlegR4', |
|||
id=17, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL4'), |
|||
18: |
|||
dict( |
|||
name='forelegL1', |
|||
id=18, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR1'), |
|||
19: |
|||
dict( |
|||
name='forelegL2', |
|||
id=19, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR2'), |
|||
20: |
|||
dict( |
|||
name='forelegL3', |
|||
id=20, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR3'), |
|||
21: |
|||
dict( |
|||
name='forelegL4', |
|||
id=21, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR4'), |
|||
22: |
|||
dict( |
|||
name='midlegL1', |
|||
id=22, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR1'), |
|||
23: |
|||
dict( |
|||
name='midlegL2', |
|||
id=23, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR2'), |
|||
24: |
|||
dict( |
|||
name='midlegL3', |
|||
id=24, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR3'), |
|||
25: |
|||
dict( |
|||
name='midlegL4', |
|||
id=25, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR4'), |
|||
26: |
|||
dict( |
|||
name='hindlegL1', |
|||
id=26, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR1'), |
|||
27: |
|||
dict( |
|||
name='hindlegL2', |
|||
id=27, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR2'), |
|||
28: |
|||
dict( |
|||
name='hindlegL3', |
|||
id=28, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR3'), |
|||
29: |
|||
dict( |
|||
name='hindlegL4', |
|||
id=29, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR4'), |
|||
30: |
|||
dict( |
|||
name='wingL', id=30, color=[255, 255, 255], type='', swap='wingR'), |
|||
31: |
|||
dict( |
|||
name='wingR', id=31, color=[255, 255, 255], type='', swap='wingL'), |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('eyeL', 'head'), id=0, color=[255, 255, 255]), |
|||
1: dict(link=('eyeR', 'head'), id=1, color=[255, 255, 255]), |
|||
2: dict(link=('neck', 'head'), id=2, color=[255, 255, 255]), |
|||
3: dict(link=('thorax', 'neck'), id=3, color=[255, 255, 255]), |
|||
4: dict(link=('abdomen', 'thorax'), id=4, color=[255, 255, 255]), |
|||
5: dict(link=('forelegR2', 'forelegR1'), id=5, color=[255, 255, 255]), |
|||
6: dict(link=('forelegR3', 'forelegR2'), id=6, color=[255, 255, 255]), |
|||
7: dict(link=('forelegR4', 'forelegR3'), id=7, color=[255, 255, 255]), |
|||
8: dict(link=('midlegR2', 'midlegR1'), id=8, color=[255, 255, 255]), |
|||
9: dict(link=('midlegR3', 'midlegR2'), id=9, color=[255, 255, 255]), |
|||
10: dict(link=('midlegR4', 'midlegR3'), id=10, color=[255, 255, 255]), |
|||
11: |
|||
dict(link=('hindlegR2', 'hindlegR1'), id=11, color=[255, 255, 255]), |
|||
12: |
|||
dict(link=('hindlegR3', 'hindlegR2'), id=12, color=[255, 255, 255]), |
|||
13: |
|||
dict(link=('hindlegR4', 'hindlegR3'), id=13, color=[255, 255, 255]), |
|||
14: |
|||
dict(link=('forelegL2', 'forelegL1'), id=14, color=[255, 255, 255]), |
|||
15: |
|||
dict(link=('forelegL3', 'forelegL2'), id=15, color=[255, 255, 255]), |
|||
16: |
|||
dict(link=('forelegL4', 'forelegL3'), id=16, color=[255, 255, 255]), |
|||
17: dict(link=('midlegL2', 'midlegL1'), id=17, color=[255, 255, 255]), |
|||
18: dict(link=('midlegL3', 'midlegL2'), id=18, color=[255, 255, 255]), |
|||
19: dict(link=('midlegL4', 'midlegL3'), id=19, color=[255, 255, 255]), |
|||
20: |
|||
dict(link=('hindlegL2', 'hindlegL1'), id=20, color=[255, 255, 255]), |
|||
21: |
|||
dict(link=('hindlegL3', 'hindlegL2'), id=21, color=[255, 255, 255]), |
|||
22: |
|||
dict(link=('hindlegL4', 'hindlegL3'), id=22, color=[255, 255, 255]), |
|||
23: dict(link=('wingL', 'neck'), id=23, color=[255, 255, 255]), |
|||
24: dict(link=('wingR', 'neck'), id=24, color=[255, 255, 255]) |
|||
}, |
|||
joint_weights=[1.] * 32, |
|||
sigmas=[]) |
|||
@ -0,0 +1,144 @@ |
|||
dataset_info = dict( |
|||
dataset_name='freihand', |
|||
paper_info=dict( |
|||
author='Zimmermann, Christian and Ceylan, Duygu and ' |
|||
'Yang, Jimei and Russell, Bryan and ' |
|||
'Argus, Max and Brox, Thomas', |
|||
title='Freihand: A dataset for markerless capture of hand pose ' |
|||
'and shape from single rgb images', |
|||
container='Proceedings of the IEEE International ' |
|||
'Conference on Computer Vision', |
|||
year='2019', |
|||
homepage='https://lmb.informatik.uni-freiburg.de/projects/freihand/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''), |
|||
2: |
|||
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''), |
|||
3: |
|||
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''), |
|||
4: |
|||
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''), |
|||
5: |
|||
dict( |
|||
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''), |
|||
6: |
|||
dict( |
|||
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''), |
|||
7: |
|||
dict( |
|||
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''), |
|||
8: |
|||
dict( |
|||
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''), |
|||
9: |
|||
dict( |
|||
name='middle_finger1', |
|||
id=9, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='middle_finger2', |
|||
id=10, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
11: |
|||
dict( |
|||
name='middle_finger3', |
|||
id=11, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
12: |
|||
dict( |
|||
name='middle_finger4', |
|||
id=12, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
13: |
|||
dict( |
|||
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''), |
|||
14: |
|||
dict( |
|||
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''), |
|||
15: |
|||
dict( |
|||
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''), |
|||
16: |
|||
dict( |
|||
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''), |
|||
17: |
|||
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''), |
|||
18: |
|||
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''), |
|||
19: |
|||
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''), |
|||
20: |
|||
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]), |
|||
5: |
|||
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]), |
|||
6: |
|||
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]), |
|||
7: |
|||
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]), |
|||
8: |
|||
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]), |
|||
9: |
|||
dict( |
|||
link=('middle_finger1', 'middle_finger2'), |
|||
id=9, |
|||
color=[102, 178, 255]), |
|||
10: |
|||
dict( |
|||
link=('middle_finger2', 'middle_finger3'), |
|||
id=10, |
|||
color=[102, 178, 255]), |
|||
11: |
|||
dict( |
|||
link=('middle_finger3', 'middle_finger4'), |
|||
id=11, |
|||
color=[102, 178, 255]), |
|||
12: |
|||
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]), |
|||
13: |
|||
dict( |
|||
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]), |
|||
14: |
|||
dict( |
|||
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]), |
|||
15: |
|||
dict( |
|||
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]), |
|||
16: |
|||
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]), |
|||
17: |
|||
dict( |
|||
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]), |
|||
18: |
|||
dict( |
|||
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]), |
|||
19: |
|||
dict( |
|||
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[1.] * 21, |
|||
sigmas=[]) |
|||
@ -0,0 +1,152 @@ |
|||
dataset_info = dict( |
|||
dataset_name='h36m', |
|||
paper_info=dict( |
|||
author='Ionescu, Catalin and Papava, Dragos and ' |
|||
'Olaru, Vlad and Sminchisescu, Cristian', |
|||
title='Human3.6M: Large Scale Datasets and Predictive ' |
|||
'Methods for 3D Human Sensing in Natural Environments', |
|||
container='IEEE Transactions on Pattern Analysis and ' |
|||
'Machine Intelligence', |
|||
year='2014', |
|||
homepage='http://vision.imar.ro/human3.6m/description.php', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='root', id=0, color=[51, 153, 255], type='lower', swap=''), |
|||
1: |
|||
dict( |
|||
name='right_hip', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
2: |
|||
dict( |
|||
name='right_knee', |
|||
id=2, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
3: |
|||
dict( |
|||
name='right_foot', |
|||
id=3, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_foot'), |
|||
4: |
|||
dict( |
|||
name='left_hip', |
|||
id=4, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
5: |
|||
dict( |
|||
name='left_knee', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
6: |
|||
dict( |
|||
name='left_foot', |
|||
id=6, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_foot'), |
|||
7: |
|||
dict(name='spine', id=7, color=[51, 153, 255], type='upper', swap=''), |
|||
8: |
|||
dict(name='thorax', id=8, color=[51, 153, 255], type='upper', swap=''), |
|||
9: |
|||
dict( |
|||
name='neck_base', |
|||
id=9, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap=''), |
|||
10: |
|||
dict(name='head', id=10, color=[51, 153, 255], type='upper', swap=''), |
|||
11: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
12: |
|||
dict( |
|||
name='left_elbow', |
|||
id=12, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
13: |
|||
dict( |
|||
name='left_wrist', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
14: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=14, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
15: |
|||
dict( |
|||
name='right_elbow', |
|||
id=15, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
16: |
|||
dict( |
|||
name='right_wrist', |
|||
id=16, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('root', 'left_hip'), id=0, color=[0, 255, 0]), |
|||
1: |
|||
dict(link=('left_hip', 'left_knee'), id=1, color=[0, 255, 0]), |
|||
2: |
|||
dict(link=('left_knee', 'left_foot'), id=2, color=[0, 255, 0]), |
|||
3: |
|||
dict(link=('root', 'right_hip'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('right_hip', 'right_knee'), id=4, color=[255, 128, 0]), |
|||
5: |
|||
dict(link=('right_knee', 'right_foot'), id=5, color=[255, 128, 0]), |
|||
6: |
|||
dict(link=('root', 'spine'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict(link=('spine', 'thorax'), id=7, color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('thorax', 'neck_base'), id=8, color=[51, 153, 255]), |
|||
9: |
|||
dict(link=('neck_base', 'head'), id=9, color=[51, 153, 255]), |
|||
10: |
|||
dict(link=('thorax', 'left_shoulder'), id=10, color=[0, 255, 0]), |
|||
11: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=11, color=[0, 255, 0]), |
|||
12: |
|||
dict(link=('left_elbow', 'left_wrist'), id=12, color=[0, 255, 0]), |
|||
13: |
|||
dict(link=('thorax', 'right_shoulder'), id=13, color=[255, 128, 0]), |
|||
14: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=14, color=[255, 128, |
|||
0]), |
|||
15: |
|||
dict(link=('right_elbow', 'right_wrist'), id=15, color=[255, 128, 0]) |
|||
}, |
|||
joint_weights=[1.] * 17, |
|||
sigmas=[], |
|||
stats_info=dict(bbox_center=(528., 427.), bbox_scale=400.)) |
|||
File diff suppressed because it is too large
@ -0,0 +1,201 @@ |
|||
dataset_info = dict( |
|||
dataset_name='horse10', |
|||
paper_info=dict( |
|||
author='Mathis, Alexander and Biasi, Thomas and ' |
|||
'Schneider, Steffen and ' |
|||
'Yuksekgonul, Mert and Rogers, Byron and ' |
|||
'Bethge, Matthias and ' |
|||
'Mathis, Mackenzie W', |
|||
title='Pretraining boosts out-of-domain robustness ' |
|||
'for pose estimation', |
|||
container='Proceedings of the IEEE/CVF Winter Conference on ' |
|||
'Applications of Computer Vision', |
|||
year='2021', |
|||
homepage='http://www.mackenziemathislab.org/horse10', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='Nose', id=0, color=[255, 153, 255], type='upper', swap=''), |
|||
1: |
|||
dict(name='Eye', id=1, color=[255, 153, 255], type='upper', swap=''), |
|||
2: |
|||
dict( |
|||
name='Nearknee', |
|||
id=2, |
|||
color=[255, 102, 255], |
|||
type='upper', |
|||
swap=''), |
|||
3: |
|||
dict( |
|||
name='Nearfrontfetlock', |
|||
id=3, |
|||
color=[255, 102, 255], |
|||
type='upper', |
|||
swap=''), |
|||
4: |
|||
dict( |
|||
name='Nearfrontfoot', |
|||
id=4, |
|||
color=[255, 102, 255], |
|||
type='upper', |
|||
swap=''), |
|||
5: |
|||
dict( |
|||
name='Offknee', id=5, color=[255, 102, 255], type='upper', |
|||
swap=''), |
|||
6: |
|||
dict( |
|||
name='Offfrontfetlock', |
|||
id=6, |
|||
color=[255, 102, 255], |
|||
type='upper', |
|||
swap=''), |
|||
7: |
|||
dict( |
|||
name='Offfrontfoot', |
|||
id=7, |
|||
color=[255, 102, 255], |
|||
type='upper', |
|||
swap=''), |
|||
8: |
|||
dict( |
|||
name='Shoulder', |
|||
id=8, |
|||
color=[255, 153, 255], |
|||
type='upper', |
|||
swap=''), |
|||
9: |
|||
dict( |
|||
name='Midshoulder', |
|||
id=9, |
|||
color=[255, 153, 255], |
|||
type='upper', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='Elbow', id=10, color=[255, 153, 255], type='upper', swap=''), |
|||
11: |
|||
dict( |
|||
name='Girth', id=11, color=[255, 153, 255], type='upper', swap=''), |
|||
12: |
|||
dict( |
|||
name='Wither', id=12, color=[255, 153, 255], type='upper', |
|||
swap=''), |
|||
13: |
|||
dict( |
|||
name='Nearhindhock', |
|||
id=13, |
|||
color=[255, 51, 255], |
|||
type='lower', |
|||
swap=''), |
|||
14: |
|||
dict( |
|||
name='Nearhindfetlock', |
|||
id=14, |
|||
color=[255, 51, 255], |
|||
type='lower', |
|||
swap=''), |
|||
15: |
|||
dict( |
|||
name='Nearhindfoot', |
|||
id=15, |
|||
color=[255, 51, 255], |
|||
type='lower', |
|||
swap=''), |
|||
16: |
|||
dict(name='Hip', id=16, color=[255, 153, 255], type='lower', swap=''), |
|||
17: |
|||
dict( |
|||
name='Stifle', id=17, color=[255, 153, 255], type='lower', |
|||
swap=''), |
|||
18: |
|||
dict( |
|||
name='Offhindhock', |
|||
id=18, |
|||
color=[255, 51, 255], |
|||
type='lower', |
|||
swap=''), |
|||
19: |
|||
dict( |
|||
name='Offhindfetlock', |
|||
id=19, |
|||
color=[255, 51, 255], |
|||
type='lower', |
|||
swap=''), |
|||
20: |
|||
dict( |
|||
name='Offhindfoot', |
|||
id=20, |
|||
color=[255, 51, 255], |
|||
type='lower', |
|||
swap=''), |
|||
21: |
|||
dict( |
|||
name='Ischium', |
|||
id=21, |
|||
color=[255, 153, 255], |
|||
type='lower', |
|||
swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('Nose', 'Eye'), id=0, color=[255, 153, 255]), |
|||
1: |
|||
dict(link=('Eye', 'Wither'), id=1, color=[255, 153, 255]), |
|||
2: |
|||
dict(link=('Wither', 'Hip'), id=2, color=[255, 153, 255]), |
|||
3: |
|||
dict(link=('Hip', 'Ischium'), id=3, color=[255, 153, 255]), |
|||
4: |
|||
dict(link=('Ischium', 'Stifle'), id=4, color=[255, 153, 255]), |
|||
5: |
|||
dict(link=('Stifle', 'Girth'), id=5, color=[255, 153, 255]), |
|||
6: |
|||
dict(link=('Girth', 'Elbow'), id=6, color=[255, 153, 255]), |
|||
7: |
|||
dict(link=('Elbow', 'Shoulder'), id=7, color=[255, 153, 255]), |
|||
8: |
|||
dict(link=('Shoulder', 'Midshoulder'), id=8, color=[255, 153, 255]), |
|||
9: |
|||
dict(link=('Midshoulder', 'Wither'), id=9, color=[255, 153, 255]), |
|||
10: |
|||
dict( |
|||
link=('Nearknee', 'Nearfrontfetlock'), |
|||
id=10, |
|||
color=[255, 102, 255]), |
|||
11: |
|||
dict( |
|||
link=('Nearfrontfetlock', 'Nearfrontfoot'), |
|||
id=11, |
|||
color=[255, 102, 255]), |
|||
12: |
|||
dict( |
|||
link=('Offknee', 'Offfrontfetlock'), id=12, color=[255, 102, 255]), |
|||
13: |
|||
dict( |
|||
link=('Offfrontfetlock', 'Offfrontfoot'), |
|||
id=13, |
|||
color=[255, 102, 255]), |
|||
14: |
|||
dict( |
|||
link=('Nearhindhock', 'Nearhindfetlock'), |
|||
id=14, |
|||
color=[255, 51, 255]), |
|||
15: |
|||
dict( |
|||
link=('Nearhindfetlock', 'Nearhindfoot'), |
|||
id=15, |
|||
color=[255, 51, 255]), |
|||
16: |
|||
dict( |
|||
link=('Offhindhock', 'Offhindfetlock'), |
|||
id=16, |
|||
color=[255, 51, 255]), |
|||
17: |
|||
dict( |
|||
link=('Offhindfetlock', 'Offhindfoot'), |
|||
id=17, |
|||
color=[255, 51, 255]) |
|||
}, |
|||
joint_weights=[1.] * 22, |
|||
sigmas=[]) |
|||
@ -0,0 +1,142 @@ |
|||
dataset_info = dict( |
|||
dataset_name='interhand2d', |
|||
paper_info=dict( |
|||
author='Moon, Gyeongsik and Yu, Shoou-I and Wen, He and ' |
|||
'Shiratori, Takaaki and Lee, Kyoung Mu', |
|||
title='InterHand2.6M: A dataset and baseline for 3D ' |
|||
'interacting hand pose estimation from a single RGB image', |
|||
container='arXiv', |
|||
year='2020', |
|||
homepage='https://mks0601.github.io/InterHand2.6M/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='thumb4', id=0, color=[255, 128, 0], type='', swap=''), |
|||
1: |
|||
dict(name='thumb3', id=1, color=[255, 128, 0], type='', swap=''), |
|||
2: |
|||
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''), |
|||
3: |
|||
dict(name='thumb1', id=3, color=[255, 128, 0], type='', swap=''), |
|||
4: |
|||
dict( |
|||
name='forefinger4', id=4, color=[255, 153, 255], type='', swap=''), |
|||
5: |
|||
dict( |
|||
name='forefinger3', id=5, color=[255, 153, 255], type='', swap=''), |
|||
6: |
|||
dict( |
|||
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''), |
|||
7: |
|||
dict( |
|||
name='forefinger1', id=7, color=[255, 153, 255], type='', swap=''), |
|||
8: |
|||
dict( |
|||
name='middle_finger4', |
|||
id=8, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
9: |
|||
dict( |
|||
name='middle_finger3', |
|||
id=9, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='middle_finger2', |
|||
id=10, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
11: |
|||
dict( |
|||
name='middle_finger1', |
|||
id=11, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
12: |
|||
dict( |
|||
name='ring_finger4', id=12, color=[255, 51, 51], type='', swap=''), |
|||
13: |
|||
dict( |
|||
name='ring_finger3', id=13, color=[255, 51, 51], type='', swap=''), |
|||
14: |
|||
dict( |
|||
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''), |
|||
15: |
|||
dict( |
|||
name='ring_finger1', id=15, color=[255, 51, 51], type='', swap=''), |
|||
16: |
|||
dict(name='pinky_finger4', id=16, color=[0, 255, 0], type='', swap=''), |
|||
17: |
|||
dict(name='pinky_finger3', id=17, color=[0, 255, 0], type='', swap=''), |
|||
18: |
|||
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''), |
|||
19: |
|||
dict(name='pinky_finger1', id=19, color=[0, 255, 0], type='', swap=''), |
|||
20: |
|||
dict(name='wrist', id=20, color=[255, 255, 255], type='', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]), |
|||
5: |
|||
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]), |
|||
6: |
|||
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]), |
|||
7: |
|||
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]), |
|||
8: |
|||
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]), |
|||
9: |
|||
dict( |
|||
link=('middle_finger1', 'middle_finger2'), |
|||
id=9, |
|||
color=[102, 178, 255]), |
|||
10: |
|||
dict( |
|||
link=('middle_finger2', 'middle_finger3'), |
|||
id=10, |
|||
color=[102, 178, 255]), |
|||
11: |
|||
dict( |
|||
link=('middle_finger3', 'middle_finger4'), |
|||
id=11, |
|||
color=[102, 178, 255]), |
|||
12: |
|||
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]), |
|||
13: |
|||
dict( |
|||
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]), |
|||
14: |
|||
dict( |
|||
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]), |
|||
15: |
|||
dict( |
|||
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]), |
|||
16: |
|||
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]), |
|||
17: |
|||
dict( |
|||
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]), |
|||
18: |
|||
dict( |
|||
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]), |
|||
19: |
|||
dict( |
|||
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[1.] * 21, |
|||
sigmas=[]) |
|||
@ -0,0 +1,487 @@ |
|||
dataset_info = dict( |
|||
dataset_name='interhand3d', |
|||
paper_info=dict( |
|||
author='Moon, Gyeongsik and Yu, Shoou-I and Wen, He and ' |
|||
'Shiratori, Takaaki and Lee, Kyoung Mu', |
|||
title='InterHand2.6M: A dataset and baseline for 3D ' |
|||
'interacting hand pose estimation from a single RGB image', |
|||
container='arXiv', |
|||
year='2020', |
|||
homepage='https://mks0601.github.io/InterHand2.6M/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='right_thumb4', |
|||
id=0, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='left_thumb4'), |
|||
1: |
|||
dict( |
|||
name='right_thumb3', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='left_thumb3'), |
|||
2: |
|||
dict( |
|||
name='right_thumb2', |
|||
id=2, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='left_thumb2'), |
|||
3: |
|||
dict( |
|||
name='right_thumb1', |
|||
id=3, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='left_thumb1'), |
|||
4: |
|||
dict( |
|||
name='right_forefinger4', |
|||
id=4, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='left_forefinger4'), |
|||
5: |
|||
dict( |
|||
name='right_forefinger3', |
|||
id=5, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='left_forefinger3'), |
|||
6: |
|||
dict( |
|||
name='right_forefinger2', |
|||
id=6, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='left_forefinger2'), |
|||
7: |
|||
dict( |
|||
name='right_forefinger1', |
|||
id=7, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='left_forefinger1'), |
|||
8: |
|||
dict( |
|||
name='right_middle_finger4', |
|||
id=8, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='left_middle_finger4'), |
|||
9: |
|||
dict( |
|||
name='right_middle_finger3', |
|||
id=9, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='left_middle_finger3'), |
|||
10: |
|||
dict( |
|||
name='right_middle_finger2', |
|||
id=10, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='left_middle_finger2'), |
|||
11: |
|||
dict( |
|||
name='right_middle_finger1', |
|||
id=11, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='left_middle_finger1'), |
|||
12: |
|||
dict( |
|||
name='right_ring_finger4', |
|||
id=12, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='left_ring_finger4'), |
|||
13: |
|||
dict( |
|||
name='right_ring_finger3', |
|||
id=13, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='left_ring_finger3'), |
|||
14: |
|||
dict( |
|||
name='right_ring_finger2', |
|||
id=14, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='left_ring_finger2'), |
|||
15: |
|||
dict( |
|||
name='right_ring_finger1', |
|||
id=15, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='left_ring_finger1'), |
|||
16: |
|||
dict( |
|||
name='right_pinky_finger4', |
|||
id=16, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='left_pinky_finger4'), |
|||
17: |
|||
dict( |
|||
name='right_pinky_finger3', |
|||
id=17, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='left_pinky_finger3'), |
|||
18: |
|||
dict( |
|||
name='right_pinky_finger2', |
|||
id=18, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='left_pinky_finger2'), |
|||
19: |
|||
dict( |
|||
name='right_pinky_finger1', |
|||
id=19, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='left_pinky_finger1'), |
|||
20: |
|||
dict( |
|||
name='right_wrist', |
|||
id=20, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='left_wrist'), |
|||
21: |
|||
dict( |
|||
name='left_thumb4', |
|||
id=21, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='right_thumb4'), |
|||
22: |
|||
dict( |
|||
name='left_thumb3', |
|||
id=22, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='right_thumb3'), |
|||
23: |
|||
dict( |
|||
name='left_thumb2', |
|||
id=23, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='right_thumb2'), |
|||
24: |
|||
dict( |
|||
name='left_thumb1', |
|||
id=24, |
|||
color=[255, 128, 0], |
|||
type='', |
|||
swap='right_thumb1'), |
|||
25: |
|||
dict( |
|||
name='left_forefinger4', |
|||
id=25, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='right_forefinger4'), |
|||
26: |
|||
dict( |
|||
name='left_forefinger3', |
|||
id=26, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='right_forefinger3'), |
|||
27: |
|||
dict( |
|||
name='left_forefinger2', |
|||
id=27, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='right_forefinger2'), |
|||
28: |
|||
dict( |
|||
name='left_forefinger1', |
|||
id=28, |
|||
color=[255, 153, 255], |
|||
type='', |
|||
swap='right_forefinger1'), |
|||
29: |
|||
dict( |
|||
name='left_middle_finger4', |
|||
id=29, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='right_middle_finger4'), |
|||
30: |
|||
dict( |
|||
name='left_middle_finger3', |
|||
id=30, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='right_middle_finger3'), |
|||
31: |
|||
dict( |
|||
name='left_middle_finger2', |
|||
id=31, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='right_middle_finger2'), |
|||
32: |
|||
dict( |
|||
name='left_middle_finger1', |
|||
id=32, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap='right_middle_finger1'), |
|||
33: |
|||
dict( |
|||
name='left_ring_finger4', |
|||
id=33, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='right_ring_finger4'), |
|||
34: |
|||
dict( |
|||
name='left_ring_finger3', |
|||
id=34, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='right_ring_finger3'), |
|||
35: |
|||
dict( |
|||
name='left_ring_finger2', |
|||
id=35, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='right_ring_finger2'), |
|||
36: |
|||
dict( |
|||
name='left_ring_finger1', |
|||
id=36, |
|||
color=[255, 51, 51], |
|||
type='', |
|||
swap='right_ring_finger1'), |
|||
37: |
|||
dict( |
|||
name='left_pinky_finger4', |
|||
id=37, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='right_pinky_finger4'), |
|||
38: |
|||
dict( |
|||
name='left_pinky_finger3', |
|||
id=38, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='right_pinky_finger3'), |
|||
39: |
|||
dict( |
|||
name='left_pinky_finger2', |
|||
id=39, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='right_pinky_finger2'), |
|||
40: |
|||
dict( |
|||
name='left_pinky_finger1', |
|||
id=40, |
|||
color=[0, 255, 0], |
|||
type='', |
|||
swap='right_pinky_finger1'), |
|||
41: |
|||
dict( |
|||
name='left_wrist', |
|||
id=41, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='right_wrist'), |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('right_wrist', 'right_thumb1'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('right_thumb1', 'right_thumb2'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('right_thumb2', 'right_thumb3'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('right_thumb3', 'right_thumb4'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict( |
|||
link=('right_wrist', 'right_forefinger1'), |
|||
id=4, |
|||
color=[255, 153, 255]), |
|||
5: |
|||
dict( |
|||
link=('right_forefinger1', 'right_forefinger2'), |
|||
id=5, |
|||
color=[255, 153, 255]), |
|||
6: |
|||
dict( |
|||
link=('right_forefinger2', 'right_forefinger3'), |
|||
id=6, |
|||
color=[255, 153, 255]), |
|||
7: |
|||
dict( |
|||
link=('right_forefinger3', 'right_forefinger4'), |
|||
id=7, |
|||
color=[255, 153, 255]), |
|||
8: |
|||
dict( |
|||
link=('right_wrist', 'right_middle_finger1'), |
|||
id=8, |
|||
color=[102, 178, 255]), |
|||
9: |
|||
dict( |
|||
link=('right_middle_finger1', 'right_middle_finger2'), |
|||
id=9, |
|||
color=[102, 178, 255]), |
|||
10: |
|||
dict( |
|||
link=('right_middle_finger2', 'right_middle_finger3'), |
|||
id=10, |
|||
color=[102, 178, 255]), |
|||
11: |
|||
dict( |
|||
link=('right_middle_finger3', 'right_middle_finger4'), |
|||
id=11, |
|||
color=[102, 178, 255]), |
|||
12: |
|||
dict( |
|||
link=('right_wrist', 'right_ring_finger1'), |
|||
id=12, |
|||
color=[255, 51, 51]), |
|||
13: |
|||
dict( |
|||
link=('right_ring_finger1', 'right_ring_finger2'), |
|||
id=13, |
|||
color=[255, 51, 51]), |
|||
14: |
|||
dict( |
|||
link=('right_ring_finger2', 'right_ring_finger3'), |
|||
id=14, |
|||
color=[255, 51, 51]), |
|||
15: |
|||
dict( |
|||
link=('right_ring_finger3', 'right_ring_finger4'), |
|||
id=15, |
|||
color=[255, 51, 51]), |
|||
16: |
|||
dict( |
|||
link=('right_wrist', 'right_pinky_finger1'), |
|||
id=16, |
|||
color=[0, 255, 0]), |
|||
17: |
|||
dict( |
|||
link=('right_pinky_finger1', 'right_pinky_finger2'), |
|||
id=17, |
|||
color=[0, 255, 0]), |
|||
18: |
|||
dict( |
|||
link=('right_pinky_finger2', 'right_pinky_finger3'), |
|||
id=18, |
|||
color=[0, 255, 0]), |
|||
19: |
|||
dict( |
|||
link=('right_pinky_finger3', 'right_pinky_finger4'), |
|||
id=19, |
|||
color=[0, 255, 0]), |
|||
20: |
|||
dict(link=('left_wrist', 'left_thumb1'), id=20, color=[255, 128, 0]), |
|||
21: |
|||
dict(link=('left_thumb1', 'left_thumb2'), id=21, color=[255, 128, 0]), |
|||
22: |
|||
dict(link=('left_thumb2', 'left_thumb3'), id=22, color=[255, 128, 0]), |
|||
23: |
|||
dict(link=('left_thumb3', 'left_thumb4'), id=23, color=[255, 128, 0]), |
|||
24: |
|||
dict( |
|||
link=('left_wrist', 'left_forefinger1'), |
|||
id=24, |
|||
color=[255, 153, 255]), |
|||
25: |
|||
dict( |
|||
link=('left_forefinger1', 'left_forefinger2'), |
|||
id=25, |
|||
color=[255, 153, 255]), |
|||
26: |
|||
dict( |
|||
link=('left_forefinger2', 'left_forefinger3'), |
|||
id=26, |
|||
color=[255, 153, 255]), |
|||
27: |
|||
dict( |
|||
link=('left_forefinger3', 'left_forefinger4'), |
|||
id=27, |
|||
color=[255, 153, 255]), |
|||
28: |
|||
dict( |
|||
link=('left_wrist', 'left_middle_finger1'), |
|||
id=28, |
|||
color=[102, 178, 255]), |
|||
29: |
|||
dict( |
|||
link=('left_middle_finger1', 'left_middle_finger2'), |
|||
id=29, |
|||
color=[102, 178, 255]), |
|||
30: |
|||
dict( |
|||
link=('left_middle_finger2', 'left_middle_finger3'), |
|||
id=30, |
|||
color=[102, 178, 255]), |
|||
31: |
|||
dict( |
|||
link=('left_middle_finger3', 'left_middle_finger4'), |
|||
id=31, |
|||
color=[102, 178, 255]), |
|||
32: |
|||
dict( |
|||
link=('left_wrist', 'left_ring_finger1'), |
|||
id=32, |
|||
color=[255, 51, 51]), |
|||
33: |
|||
dict( |
|||
link=('left_ring_finger1', 'left_ring_finger2'), |
|||
id=33, |
|||
color=[255, 51, 51]), |
|||
34: |
|||
dict( |
|||
link=('left_ring_finger2', 'left_ring_finger3'), |
|||
id=34, |
|||
color=[255, 51, 51]), |
|||
35: |
|||
dict( |
|||
link=('left_ring_finger3', 'left_ring_finger4'), |
|||
id=35, |
|||
color=[255, 51, 51]), |
|||
36: |
|||
dict( |
|||
link=('left_wrist', 'left_pinky_finger1'), |
|||
id=36, |
|||
color=[0, 255, 0]), |
|||
37: |
|||
dict( |
|||
link=('left_pinky_finger1', 'left_pinky_finger2'), |
|||
id=37, |
|||
color=[0, 255, 0]), |
|||
38: |
|||
dict( |
|||
link=('left_pinky_finger2', 'left_pinky_finger3'), |
|||
id=38, |
|||
color=[0, 255, 0]), |
|||
39: |
|||
dict( |
|||
link=('left_pinky_finger3', 'left_pinky_finger4'), |
|||
id=39, |
|||
color=[0, 255, 0]), |
|||
}, |
|||
joint_weights=[1.] * 42, |
|||
sigmas=[]) |
|||
@ -0,0 +1,129 @@ |
|||
dataset_info = dict( |
|||
dataset_name='jhmdb', |
|||
paper_info=dict( |
|||
author='H. Jhuang and J. Gall and S. Zuffi and ' |
|||
'C. Schmid and M. J. Black', |
|||
title='Towards understanding action recognition', |
|||
container='International Conf. on Computer Vision (ICCV)', |
|||
year='2013', |
|||
homepage='http://jhmdb.is.tue.mpg.de/dataset', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='neck', id=0, color=[255, 128, 0], type='upper', swap=''), |
|||
1: |
|||
dict(name='belly', id=1, color=[255, 128, 0], type='upper', swap=''), |
|||
2: |
|||
dict(name='head', id=2, color=[255, 128, 0], type='upper', swap=''), |
|||
3: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=3, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
4: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=4, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
5: |
|||
dict( |
|||
name='right_hip', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
6: |
|||
dict( |
|||
name='left_hip', |
|||
id=6, |
|||
color=[51, 153, 255], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
7: |
|||
dict( |
|||
name='right_elbow', |
|||
id=7, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
8: |
|||
dict( |
|||
name='left_elbow', |
|||
id=8, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
9: |
|||
dict( |
|||
name='right_knee', |
|||
id=9, |
|||
color=[51, 153, 255], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
10: |
|||
dict( |
|||
name='left_knee', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
11: |
|||
dict( |
|||
name='right_wrist', |
|||
id=11, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
12: |
|||
dict( |
|||
name='left_wrist', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
13: |
|||
dict( |
|||
name='right_ankle', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
14: |
|||
dict( |
|||
name='left_ankle', |
|||
id=14, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle') |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]), |
|||
1: dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]), |
|||
2: dict(link=('right_hip', 'belly'), id=2, color=[255, 128, 0]), |
|||
3: dict(link=('belly', 'left_hip'), id=3, color=[0, 255, 0]), |
|||
4: dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]), |
|||
5: dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]), |
|||
6: dict(link=('belly', 'neck'), id=6, color=[51, 153, 255]), |
|||
7: dict(link=('neck', 'head'), id=7, color=[51, 153, 255]), |
|||
8: dict(link=('neck', 'right_shoulder'), id=8, color=[255, 128, 0]), |
|||
9: dict( |
|||
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict(link=('right_elbow', 'right_wrist'), id=10, color=[255, 128, 0]), |
|||
11: dict(link=('neck', 'left_shoulder'), id=11, color=[0, 255, 0]), |
|||
12: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=12, color=[0, 255, 0]), |
|||
13: dict(link=('left_elbow', 'left_wrist'), id=13, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[ |
|||
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.2, 1.2, 1.5, 1.5, 1.5, 1.5 |
|||
], |
|||
# Adapted from COCO dataset. |
|||
sigmas=[ |
|||
0.025, 0.107, 0.025, 0.079, 0.079, 0.107, 0.107, 0.072, 0.072, 0.087, |
|||
0.087, 0.062, 0.062, 0.089, 0.089 |
|||
]) |
|||
@ -0,0 +1,263 @@ |
|||
dataset_info = dict( |
|||
dataset_name='locust', |
|||
paper_info=dict( |
|||
author='Graving, Jacob M and Chae, Daniel and Naik, Hemal and ' |
|||
'Li, Liang and Koger, Benjamin and Costelloe, Blair R and ' |
|||
'Couzin, Iain D', |
|||
title='DeepPoseKit, a software toolkit for fast and robust ' |
|||
'animal pose estimation using deep learning', |
|||
container='Elife', |
|||
year='2019', |
|||
homepage='https://github.com/jgraving/DeepPoseKit-Data', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='head', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='neck', id=1, color=[255, 255, 255], type='', swap=''), |
|||
2: |
|||
dict(name='thorax', id=2, color=[255, 255, 255], type='', swap=''), |
|||
3: |
|||
dict(name='abdomen1', id=3, color=[255, 255, 255], type='', swap=''), |
|||
4: |
|||
dict(name='abdomen2', id=4, color=[255, 255, 255], type='', swap=''), |
|||
5: |
|||
dict( |
|||
name='anttipL', |
|||
id=5, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='anttipR'), |
|||
6: |
|||
dict( |
|||
name='antbaseL', |
|||
id=6, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='antbaseR'), |
|||
7: |
|||
dict(name='eyeL', id=7, color=[255, 255, 255], type='', swap='eyeR'), |
|||
8: |
|||
dict( |
|||
name='forelegL1', |
|||
id=8, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR1'), |
|||
9: |
|||
dict( |
|||
name='forelegL2', |
|||
id=9, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR2'), |
|||
10: |
|||
dict( |
|||
name='forelegL3', |
|||
id=10, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR3'), |
|||
11: |
|||
dict( |
|||
name='forelegL4', |
|||
id=11, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR4'), |
|||
12: |
|||
dict( |
|||
name='midlegL1', |
|||
id=12, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR1'), |
|||
13: |
|||
dict( |
|||
name='midlegL2', |
|||
id=13, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR2'), |
|||
14: |
|||
dict( |
|||
name='midlegL3', |
|||
id=14, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR3'), |
|||
15: |
|||
dict( |
|||
name='midlegL4', |
|||
id=15, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegR4'), |
|||
16: |
|||
dict( |
|||
name='hindlegL1', |
|||
id=16, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR1'), |
|||
17: |
|||
dict( |
|||
name='hindlegL2', |
|||
id=17, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR2'), |
|||
18: |
|||
dict( |
|||
name='hindlegL3', |
|||
id=18, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR3'), |
|||
19: |
|||
dict( |
|||
name='hindlegL4', |
|||
id=19, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR4'), |
|||
20: |
|||
dict( |
|||
name='anttipR', |
|||
id=20, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='anttipL'), |
|||
21: |
|||
dict( |
|||
name='antbaseR', |
|||
id=21, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='antbaseL'), |
|||
22: |
|||
dict(name='eyeR', id=22, color=[255, 255, 255], type='', swap='eyeL'), |
|||
23: |
|||
dict( |
|||
name='forelegR1', |
|||
id=23, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL1'), |
|||
24: |
|||
dict( |
|||
name='forelegR2', |
|||
id=24, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL2'), |
|||
25: |
|||
dict( |
|||
name='forelegR3', |
|||
id=25, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL3'), |
|||
26: |
|||
dict( |
|||
name='forelegR4', |
|||
id=26, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL4'), |
|||
27: |
|||
dict( |
|||
name='midlegR1', |
|||
id=27, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL1'), |
|||
28: |
|||
dict( |
|||
name='midlegR2', |
|||
id=28, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL2'), |
|||
29: |
|||
dict( |
|||
name='midlegR3', |
|||
id=29, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL3'), |
|||
30: |
|||
dict( |
|||
name='midlegR4', |
|||
id=30, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='midlegL4'), |
|||
31: |
|||
dict( |
|||
name='hindlegR1', |
|||
id=31, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL1'), |
|||
32: |
|||
dict( |
|||
name='hindlegR2', |
|||
id=32, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL2'), |
|||
33: |
|||
dict( |
|||
name='hindlegR3', |
|||
id=33, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL3'), |
|||
34: |
|||
dict( |
|||
name='hindlegR4', |
|||
id=34, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL4') |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('neck', 'head'), id=0, color=[255, 255, 255]), |
|||
1: dict(link=('thorax', 'neck'), id=1, color=[255, 255, 255]), |
|||
2: dict(link=('abdomen1', 'thorax'), id=2, color=[255, 255, 255]), |
|||
3: dict(link=('abdomen2', 'abdomen1'), id=3, color=[255, 255, 255]), |
|||
4: dict(link=('antbaseL', 'anttipL'), id=4, color=[255, 255, 255]), |
|||
5: dict(link=('eyeL', 'antbaseL'), id=5, color=[255, 255, 255]), |
|||
6: dict(link=('forelegL2', 'forelegL1'), id=6, color=[255, 255, 255]), |
|||
7: dict(link=('forelegL3', 'forelegL2'), id=7, color=[255, 255, 255]), |
|||
8: dict(link=('forelegL4', 'forelegL3'), id=8, color=[255, 255, 255]), |
|||
9: dict(link=('midlegL2', 'midlegL1'), id=9, color=[255, 255, 255]), |
|||
10: dict(link=('midlegL3', 'midlegL2'), id=10, color=[255, 255, 255]), |
|||
11: dict(link=('midlegL4', 'midlegL3'), id=11, color=[255, 255, 255]), |
|||
12: |
|||
dict(link=('hindlegL2', 'hindlegL1'), id=12, color=[255, 255, 255]), |
|||
13: |
|||
dict(link=('hindlegL3', 'hindlegL2'), id=13, color=[255, 255, 255]), |
|||
14: |
|||
dict(link=('hindlegL4', 'hindlegL3'), id=14, color=[255, 255, 255]), |
|||
15: dict(link=('antbaseR', 'anttipR'), id=15, color=[255, 255, 255]), |
|||
16: dict(link=('eyeR', 'antbaseR'), id=16, color=[255, 255, 255]), |
|||
17: |
|||
dict(link=('forelegR2', 'forelegR1'), id=17, color=[255, 255, 255]), |
|||
18: |
|||
dict(link=('forelegR3', 'forelegR2'), id=18, color=[255, 255, 255]), |
|||
19: |
|||
dict(link=('forelegR4', 'forelegR3'), id=19, color=[255, 255, 255]), |
|||
20: dict(link=('midlegR2', 'midlegR1'), id=20, color=[255, 255, 255]), |
|||
21: dict(link=('midlegR3', 'midlegR2'), id=21, color=[255, 255, 255]), |
|||
22: dict(link=('midlegR4', 'midlegR3'), id=22, color=[255, 255, 255]), |
|||
23: |
|||
dict(link=('hindlegR2', 'hindlegR1'), id=23, color=[255, 255, 255]), |
|||
24: |
|||
dict(link=('hindlegR3', 'hindlegR2'), id=24, color=[255, 255, 255]), |
|||
25: |
|||
dict(link=('hindlegR4', 'hindlegR3'), id=25, color=[255, 255, 255]) |
|||
}, |
|||
joint_weights=[1.] * 35, |
|||
sigmas=[]) |
|||
@ -0,0 +1,183 @@ |
|||
dataset_info = dict( |
|||
dataset_name='macaque', |
|||
paper_info=dict( |
|||
author='Labuguen, Rollyn and Matsumoto, Jumpei and ' |
|||
'Negrete, Salvador and Nishimaru, Hiroshi and ' |
|||
'Nishijo, Hisao and Takada, Masahiko and ' |
|||
'Go, Yasuhiro and Inoue, Ken-ichi and Shibata, Tomohiro', |
|||
title='MacaquePose: A novel "in the wild" macaque monkey pose dataset ' |
|||
'for markerless motion capture', |
|||
container='bioRxiv', |
|||
year='2020', |
|||
homepage='http://www.pri.kyoto-u.ac.jp/datasets/' |
|||
'macaquepose/index.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''), |
|||
1: |
|||
dict( |
|||
name='left_eye', |
|||
id=1, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_eye'), |
|||
2: |
|||
dict( |
|||
name='right_eye', |
|||
id=2, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_eye'), |
|||
3: |
|||
dict( |
|||
name='left_ear', |
|||
id=3, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_ear'), |
|||
4: |
|||
dict( |
|||
name='right_ear', |
|||
id=4, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_ear'), |
|||
5: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
6: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=6, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
7: |
|||
dict( |
|||
name='left_elbow', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
8: |
|||
dict( |
|||
name='right_elbow', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
9: |
|||
dict( |
|||
name='left_wrist', |
|||
id=9, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
10: |
|||
dict( |
|||
name='right_wrist', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
11: |
|||
dict( |
|||
name='left_hip', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
12: |
|||
dict( |
|||
name='right_hip', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
13: |
|||
dict( |
|||
name='left_knee', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
14: |
|||
dict( |
|||
name='right_knee', |
|||
id=14, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
15: |
|||
dict( |
|||
name='left_ankle', |
|||
id=15, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
16: |
|||
dict( |
|||
name='right_ankle', |
|||
id=16, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]), |
|||
1: |
|||
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]), |
|||
2: |
|||
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]), |
|||
5: |
|||
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]), |
|||
6: |
|||
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict( |
|||
link=('left_shoulder', 'right_shoulder'), |
|||
id=7, |
|||
color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]), |
|||
9: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]), |
|||
11: |
|||
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]), |
|||
13: |
|||
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]), |
|||
14: |
|||
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]), |
|||
15: |
|||
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]), |
|||
16: |
|||
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]), |
|||
17: |
|||
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]), |
|||
18: |
|||
dict( |
|||
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255]) |
|||
}, |
|||
joint_weights=[ |
|||
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, |
|||
1.5 |
|||
], |
|||
sigmas=[ |
|||
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062, |
|||
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089 |
|||
]) |
|||
@ -0,0 +1,156 @@ |
|||
dataset_info = dict( |
|||
dataset_name='mhp', |
|||
paper_info=dict( |
|||
author='Zhao, Jian and Li, Jianshu and Cheng, Yu and ' |
|||
'Sim, Terence and Yan, Shuicheng and Feng, Jiashi', |
|||
title='Understanding humans in crowded scenes: ' |
|||
'Deep nested adversarial learning and a ' |
|||
'new benchmark for multi-human parsing', |
|||
container='Proceedings of the 26th ACM ' |
|||
'international conference on Multimedia', |
|||
year='2018', |
|||
homepage='https://lv-mhp.github.io/dataset', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='right_ankle', |
|||
id=0, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
1: |
|||
dict( |
|||
name='right_knee', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
2: |
|||
dict( |
|||
name='right_hip', |
|||
id=2, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
3: |
|||
dict( |
|||
name='left_hip', |
|||
id=3, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
4: |
|||
dict( |
|||
name='left_knee', |
|||
id=4, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
5: |
|||
dict( |
|||
name='left_ankle', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
6: |
|||
dict(name='pelvis', id=6, color=[51, 153, 255], type='lower', swap=''), |
|||
7: |
|||
dict(name='thorax', id=7, color=[51, 153, 255], type='upper', swap=''), |
|||
8: |
|||
dict( |
|||
name='upper_neck', |
|||
id=8, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap=''), |
|||
9: |
|||
dict( |
|||
name='head_top', id=9, color=[51, 153, 255], type='upper', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='right_wrist', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
11: |
|||
dict( |
|||
name='right_elbow', |
|||
id=11, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
12: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
13: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
14: |
|||
dict( |
|||
name='left_elbow', |
|||
id=14, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
15: |
|||
dict( |
|||
name='left_wrist', |
|||
id=15, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('right_hip', 'pelvis'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('pelvis', 'left_hip'), id=3, color=[0, 255, 0]), |
|||
4: |
|||
dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]), |
|||
5: |
|||
dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]), |
|||
6: |
|||
dict(link=('pelvis', 'thorax'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict(link=('thorax', 'upper_neck'), id=7, color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('upper_neck', 'head_top'), id=8, color=[51, 153, 255]), |
|||
9: |
|||
dict(link=('upper_neck', 'right_shoulder'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=10, color=[255, 128, |
|||
0]), |
|||
11: |
|||
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('upper_neck', 'left_shoulder'), id=12, color=[0, 255, 0]), |
|||
13: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=13, color=[0, 255, 0]), |
|||
14: |
|||
dict(link=('left_elbow', 'left_wrist'), id=14, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[ |
|||
1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5 |
|||
], |
|||
# Adapted from COCO dataset. |
|||
sigmas=[ |
|||
0.089, 0.083, 0.107, 0.107, 0.083, 0.089, 0.026, 0.026, 0.026, 0.026, |
|||
0.062, 0.072, 0.179, 0.179, 0.072, 0.062 |
|||
]) |
|||
@ -0,0 +1,132 @@ |
|||
dataset_info = dict( |
|||
dataset_name='mpi_inf_3dhp', |
|||
paper_info=dict( |
|||
author='ehta, Dushyant and Rhodin, Helge and Casas, Dan and ' |
|||
'Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and ' |
|||
'Theobalt, Christian', |
|||
title='Monocular 3D Human Pose Estimation In The Wild Using Improved ' |
|||
'CNN Supervision', |
|||
container='2017 international conference on 3D vision (3DV)', |
|||
year='2017', |
|||
homepage='http://gvv.mpi-inf.mpg.de/3dhp-dataset', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='head_top', id=0, color=[51, 153, 255], type='upper', |
|||
swap=''), |
|||
1: |
|||
dict(name='neck', id=1, color=[51, 153, 255], type='upper', swap=''), |
|||
2: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=2, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
3: |
|||
dict( |
|||
name='right_elbow', |
|||
id=3, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
4: |
|||
dict( |
|||
name='right_wrist', |
|||
id=4, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
5: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
6: |
|||
dict( |
|||
name='left_elbow', |
|||
id=6, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
7: |
|||
dict( |
|||
name='left_wrist', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
8: |
|||
dict( |
|||
name='right_hip', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
9: |
|||
dict( |
|||
name='right_knee', |
|||
id=9, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
10: |
|||
dict( |
|||
name='right_ankle', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
11: |
|||
dict( |
|||
name='left_hip', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
12: |
|||
dict( |
|||
name='left_knee', |
|||
id=12, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
13: |
|||
dict( |
|||
name='left_ankle', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
14: |
|||
dict(name='root', id=14, color=[51, 153, 255], type='lower', swap=''), |
|||
15: |
|||
dict(name='spine', id=15, color=[51, 153, 255], type='upper', swap=''), |
|||
16: |
|||
dict(name='head', id=16, color=[51, 153, 255], type='upper', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('neck', 'right_shoulder'), id=0, color=[255, 128, 0]), |
|||
1: dict( |
|||
link=('right_shoulder', 'right_elbow'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('right_elbow', 'right_wrist'), id=2, color=[255, 128, 0]), |
|||
3: dict(link=('neck', 'left_shoulder'), id=3, color=[0, 255, 0]), |
|||
4: dict(link=('left_shoulder', 'left_elbow'), id=4, color=[0, 255, 0]), |
|||
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]), |
|||
6: dict(link=('root', 'right_hip'), id=6, color=[255, 128, 0]), |
|||
7: dict(link=('right_hip', 'right_knee'), id=7, color=[255, 128, 0]), |
|||
8: dict(link=('right_knee', 'right_ankle'), id=8, color=[255, 128, 0]), |
|||
9: dict(link=('root', 'left_hip'), id=9, color=[0, 255, 0]), |
|||
10: dict(link=('left_hip', 'left_knee'), id=10, color=[0, 255, 0]), |
|||
11: dict(link=('left_knee', 'left_ankle'), id=11, color=[0, 255, 0]), |
|||
12: dict(link=('head_top', 'head'), id=12, color=[51, 153, 255]), |
|||
13: dict(link=('head', 'neck'), id=13, color=[51, 153, 255]), |
|||
14: dict(link=('neck', 'spine'), id=14, color=[51, 153, 255]), |
|||
15: dict(link=('spine', 'root'), id=15, color=[51, 153, 255]) |
|||
}, |
|||
joint_weights=[1.] * 17, |
|||
sigmas=[]) |
|||
@ -0,0 +1,155 @@ |
|||
dataset_info = dict( |
|||
dataset_name='mpii', |
|||
paper_info=dict( |
|||
author='Mykhaylo Andriluka and Leonid Pishchulin and ' |
|||
'Peter Gehler and Schiele, Bernt', |
|||
title='2D Human Pose Estimation: New Benchmark and ' |
|||
'State of the Art Analysis', |
|||
container='IEEE Conference on Computer Vision and ' |
|||
'Pattern Recognition (CVPR)', |
|||
year='2014', |
|||
homepage='http://human-pose.mpi-inf.mpg.de/', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='right_ankle', |
|||
id=0, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
1: |
|||
dict( |
|||
name='right_knee', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
2: |
|||
dict( |
|||
name='right_hip', |
|||
id=2, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
3: |
|||
dict( |
|||
name='left_hip', |
|||
id=3, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
4: |
|||
dict( |
|||
name='left_knee', |
|||
id=4, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
5: |
|||
dict( |
|||
name='left_ankle', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
6: |
|||
dict(name='pelvis', id=6, color=[51, 153, 255], type='lower', swap=''), |
|||
7: |
|||
dict(name='thorax', id=7, color=[51, 153, 255], type='upper', swap=''), |
|||
8: |
|||
dict( |
|||
name='upper_neck', |
|||
id=8, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap=''), |
|||
9: |
|||
dict( |
|||
name='head_top', id=9, color=[51, 153, 255], type='upper', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='right_wrist', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
11: |
|||
dict( |
|||
name='right_elbow', |
|||
id=11, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
12: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
13: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
14: |
|||
dict( |
|||
name='left_elbow', |
|||
id=14, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
15: |
|||
dict( |
|||
name='left_wrist', |
|||
id=15, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('right_hip', 'pelvis'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('pelvis', 'left_hip'), id=3, color=[0, 255, 0]), |
|||
4: |
|||
dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]), |
|||
5: |
|||
dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]), |
|||
6: |
|||
dict(link=('pelvis', 'thorax'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict(link=('thorax', 'upper_neck'), id=7, color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('upper_neck', 'head_top'), id=8, color=[51, 153, 255]), |
|||
9: |
|||
dict(link=('upper_neck', 'right_shoulder'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=10, color=[255, 128, |
|||
0]), |
|||
11: |
|||
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('upper_neck', 'left_shoulder'), id=12, color=[0, 255, 0]), |
|||
13: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=13, color=[0, 255, 0]), |
|||
14: |
|||
dict(link=('left_elbow', 'left_wrist'), id=14, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[ |
|||
1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5 |
|||
], |
|||
# Adapted from COCO dataset. |
|||
sigmas=[ |
|||
0.089, 0.083, 0.107, 0.107, 0.083, 0.089, 0.026, 0.026, 0.026, 0.026, |
|||
0.062, 0.072, 0.179, 0.179, 0.072, 0.062 |
|||
]) |
|||
@ -0,0 +1,380 @@ |
|||
dataset_info = dict( |
|||
dataset_name='mpii_trb', |
|||
paper_info=dict( |
|||
author='Duan, Haodong and Lin, Kwan-Yee and Jin, Sheng and ' |
|||
'Liu, Wentao and Qian, Chen and Ouyang, Wanli', |
|||
title='TRB: A Novel Triplet Representation for ' |
|||
'Understanding 2D Human Body', |
|||
container='Proceedings of the IEEE International ' |
|||
'Conference on Computer Vision', |
|||
year='2019', |
|||
homepage='https://github.com/kennymckormick/' |
|||
'Triplet-Representation-of-human-Body', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=0, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
1: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=1, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
2: |
|||
dict( |
|||
name='left_elbow', |
|||
id=2, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
3: |
|||
dict( |
|||
name='right_elbow', |
|||
id=3, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
4: |
|||
dict( |
|||
name='left_wrist', |
|||
id=4, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
5: |
|||
dict( |
|||
name='right_wrist', |
|||
id=5, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
6: |
|||
dict( |
|||
name='left_hip', |
|||
id=6, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
7: |
|||
dict( |
|||
name='right_hip', |
|||
id=7, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
8: |
|||
dict( |
|||
name='left_knee', |
|||
id=8, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
9: |
|||
dict( |
|||
name='right_knee', |
|||
id=9, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
10: |
|||
dict( |
|||
name='left_ankle', |
|||
id=10, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
11: |
|||
dict( |
|||
name='right_ankle', |
|||
id=11, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
12: |
|||
dict(name='head', id=12, color=[51, 153, 255], type='upper', swap=''), |
|||
13: |
|||
dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap=''), |
|||
14: |
|||
dict( |
|||
name='right_neck', |
|||
id=14, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='left_neck'), |
|||
15: |
|||
dict( |
|||
name='left_neck', |
|||
id=15, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='right_neck'), |
|||
16: |
|||
dict( |
|||
name='medial_right_shoulder', |
|||
id=16, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='medial_left_shoulder'), |
|||
17: |
|||
dict( |
|||
name='lateral_right_shoulder', |
|||
id=17, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='lateral_left_shoulder'), |
|||
18: |
|||
dict( |
|||
name='medial_right_bow', |
|||
id=18, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='medial_left_bow'), |
|||
19: |
|||
dict( |
|||
name='lateral_right_bow', |
|||
id=19, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='lateral_left_bow'), |
|||
20: |
|||
dict( |
|||
name='medial_right_wrist', |
|||
id=20, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='medial_left_wrist'), |
|||
21: |
|||
dict( |
|||
name='lateral_right_wrist', |
|||
id=21, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='lateral_left_wrist'), |
|||
22: |
|||
dict( |
|||
name='medial_left_shoulder', |
|||
id=22, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='medial_right_shoulder'), |
|||
23: |
|||
dict( |
|||
name='lateral_left_shoulder', |
|||
id=23, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='lateral_right_shoulder'), |
|||
24: |
|||
dict( |
|||
name='medial_left_bow', |
|||
id=24, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='medial_right_bow'), |
|||
25: |
|||
dict( |
|||
name='lateral_left_bow', |
|||
id=25, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='lateral_right_bow'), |
|||
26: |
|||
dict( |
|||
name='medial_left_wrist', |
|||
id=26, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='medial_right_wrist'), |
|||
27: |
|||
dict( |
|||
name='lateral_left_wrist', |
|||
id=27, |
|||
color=[255, 255, 255], |
|||
type='upper', |
|||
swap='lateral_right_wrist'), |
|||
28: |
|||
dict( |
|||
name='medial_right_hip', |
|||
id=28, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='medial_left_hip'), |
|||
29: |
|||
dict( |
|||
name='lateral_right_hip', |
|||
id=29, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='lateral_left_hip'), |
|||
30: |
|||
dict( |
|||
name='medial_right_knee', |
|||
id=30, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='medial_left_knee'), |
|||
31: |
|||
dict( |
|||
name='lateral_right_knee', |
|||
id=31, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='lateral_left_knee'), |
|||
32: |
|||
dict( |
|||
name='medial_right_ankle', |
|||
id=32, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='medial_left_ankle'), |
|||
33: |
|||
dict( |
|||
name='lateral_right_ankle', |
|||
id=33, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='lateral_left_ankle'), |
|||
34: |
|||
dict( |
|||
name='medial_left_hip', |
|||
id=34, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='medial_right_hip'), |
|||
35: |
|||
dict( |
|||
name='lateral_left_hip', |
|||
id=35, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='lateral_right_hip'), |
|||
36: |
|||
dict( |
|||
name='medial_left_knee', |
|||
id=36, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='medial_right_knee'), |
|||
37: |
|||
dict( |
|||
name='lateral_left_knee', |
|||
id=37, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='lateral_right_knee'), |
|||
38: |
|||
dict( |
|||
name='medial_left_ankle', |
|||
id=38, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='medial_right_ankle'), |
|||
39: |
|||
dict( |
|||
name='lateral_left_ankle', |
|||
id=39, |
|||
color=[255, 255, 255], |
|||
type='lower', |
|||
swap='lateral_right_ankle'), |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('head', 'neck'), id=0, color=[51, 153, 255]), |
|||
1: |
|||
dict(link=('neck', 'left_shoulder'), id=1, color=[51, 153, 255]), |
|||
2: |
|||
dict(link=('neck', 'right_shoulder'), id=2, color=[51, 153, 255]), |
|||
3: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=3, color=[0, 255, 0]), |
|||
4: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=4, color=[255, 128, 0]), |
|||
5: |
|||
dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]), |
|||
6: |
|||
dict(link=('right_elbow', 'right_wrist'), id=6, color=[255, 128, 0]), |
|||
7: |
|||
dict(link=('left_shoulder', 'left_hip'), id=7, color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('right_shoulder', 'right_hip'), id=8, color=[51, 153, 255]), |
|||
9: |
|||
dict(link=('left_hip', 'right_hip'), id=9, color=[51, 153, 255]), |
|||
10: |
|||
dict(link=('left_hip', 'left_knee'), id=10, color=[0, 255, 0]), |
|||
11: |
|||
dict(link=('right_hip', 'right_knee'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('left_knee', 'left_ankle'), id=12, color=[0, 255, 0]), |
|||
13: |
|||
dict(link=('right_knee', 'right_ankle'), id=13, color=[255, 128, 0]), |
|||
14: |
|||
dict(link=('right_neck', 'left_neck'), id=14, color=[255, 255, 255]), |
|||
15: |
|||
dict( |
|||
link=('medial_right_shoulder', 'lateral_right_shoulder'), |
|||
id=15, |
|||
color=[255, 255, 255]), |
|||
16: |
|||
dict( |
|||
link=('medial_right_bow', 'lateral_right_bow'), |
|||
id=16, |
|||
color=[255, 255, 255]), |
|||
17: |
|||
dict( |
|||
link=('medial_right_wrist', 'lateral_right_wrist'), |
|||
id=17, |
|||
color=[255, 255, 255]), |
|||
18: |
|||
dict( |
|||
link=('medial_left_shoulder', 'lateral_left_shoulder'), |
|||
id=18, |
|||
color=[255, 255, 255]), |
|||
19: |
|||
dict( |
|||
link=('medial_left_bow', 'lateral_left_bow'), |
|||
id=19, |
|||
color=[255, 255, 255]), |
|||
20: |
|||
dict( |
|||
link=('medial_left_wrist', 'lateral_left_wrist'), |
|||
id=20, |
|||
color=[255, 255, 255]), |
|||
21: |
|||
dict( |
|||
link=('medial_right_hip', 'lateral_right_hip'), |
|||
id=21, |
|||
color=[255, 255, 255]), |
|||
22: |
|||
dict( |
|||
link=('medial_right_knee', 'lateral_right_knee'), |
|||
id=22, |
|||
color=[255, 255, 255]), |
|||
23: |
|||
dict( |
|||
link=('medial_right_ankle', 'lateral_right_ankle'), |
|||
id=23, |
|||
color=[255, 255, 255]), |
|||
24: |
|||
dict( |
|||
link=('medial_left_hip', 'lateral_left_hip'), |
|||
id=24, |
|||
color=[255, 255, 255]), |
|||
25: |
|||
dict( |
|||
link=('medial_left_knee', 'lateral_left_knee'), |
|||
id=25, |
|||
color=[255, 255, 255]), |
|||
26: |
|||
dict( |
|||
link=('medial_left_ankle', 'lateral_left_ankle'), |
|||
id=26, |
|||
color=[255, 255, 255]) |
|||
}, |
|||
joint_weights=[1.] * 40, |
|||
sigmas=[]) |
|||
@ -0,0 +1,181 @@ |
|||
dataset_info = dict( |
|||
dataset_name='ochuman', |
|||
paper_info=dict( |
|||
author='Zhang, Song-Hai and Li, Ruilong and Dong, Xin and ' |
|||
'Rosin, Paul and Cai, Zixi and Han, Xi and ' |
|||
'Yang, Dingcheng and Huang, Haozhi and Hu, Shi-Min', |
|||
title='Pose2seg: Detection free human instance segmentation', |
|||
container='Proceedings of the IEEE conference on computer ' |
|||
'vision and pattern recognition', |
|||
year='2019', |
|||
homepage='https://github.com/liruilong940607/OCHumanApi', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''), |
|||
1: |
|||
dict( |
|||
name='left_eye', |
|||
id=1, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_eye'), |
|||
2: |
|||
dict( |
|||
name='right_eye', |
|||
id=2, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_eye'), |
|||
3: |
|||
dict( |
|||
name='left_ear', |
|||
id=3, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_ear'), |
|||
4: |
|||
dict( |
|||
name='right_ear', |
|||
id=4, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_ear'), |
|||
5: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
6: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=6, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
7: |
|||
dict( |
|||
name='left_elbow', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
8: |
|||
dict( |
|||
name='right_elbow', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
9: |
|||
dict( |
|||
name='left_wrist', |
|||
id=9, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
10: |
|||
dict( |
|||
name='right_wrist', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
11: |
|||
dict( |
|||
name='left_hip', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
12: |
|||
dict( |
|||
name='right_hip', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
13: |
|||
dict( |
|||
name='left_knee', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
14: |
|||
dict( |
|||
name='right_knee', |
|||
id=14, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
15: |
|||
dict( |
|||
name='left_ankle', |
|||
id=15, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
16: |
|||
dict( |
|||
name='right_ankle', |
|||
id=16, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]), |
|||
1: |
|||
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]), |
|||
2: |
|||
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]), |
|||
5: |
|||
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]), |
|||
6: |
|||
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict( |
|||
link=('left_shoulder', 'right_shoulder'), |
|||
id=7, |
|||
color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]), |
|||
9: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]), |
|||
11: |
|||
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]), |
|||
13: |
|||
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]), |
|||
14: |
|||
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]), |
|||
15: |
|||
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]), |
|||
16: |
|||
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]), |
|||
17: |
|||
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]), |
|||
18: |
|||
dict( |
|||
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255]) |
|||
}, |
|||
joint_weights=[ |
|||
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, |
|||
1.5 |
|||
], |
|||
sigmas=[ |
|||
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062, |
|||
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089 |
|||
]) |
|||
@ -0,0 +1,142 @@ |
|||
dataset_info = dict( |
|||
dataset_name='onehand10k', |
|||
paper_info=dict( |
|||
author='Wang, Yangang and Peng, Cong and Liu, Yebin', |
|||
title='Mask-pose cascaded cnn for 2d hand pose estimation ' |
|||
'from single color image', |
|||
container='IEEE Transactions on Circuits and Systems ' |
|||
'for Video Technology', |
|||
year='2018', |
|||
homepage='https://www.yangangwang.com/papers/WANG-MCC-2018-10.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''), |
|||
2: |
|||
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''), |
|||
3: |
|||
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''), |
|||
4: |
|||
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''), |
|||
5: |
|||
dict( |
|||
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''), |
|||
6: |
|||
dict( |
|||
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''), |
|||
7: |
|||
dict( |
|||
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''), |
|||
8: |
|||
dict( |
|||
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''), |
|||
9: |
|||
dict( |
|||
name='middle_finger1', |
|||
id=9, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='middle_finger2', |
|||
id=10, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
11: |
|||
dict( |
|||
name='middle_finger3', |
|||
id=11, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
12: |
|||
dict( |
|||
name='middle_finger4', |
|||
id=12, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
13: |
|||
dict( |
|||
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''), |
|||
14: |
|||
dict( |
|||
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''), |
|||
15: |
|||
dict( |
|||
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''), |
|||
16: |
|||
dict( |
|||
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''), |
|||
17: |
|||
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''), |
|||
18: |
|||
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''), |
|||
19: |
|||
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''), |
|||
20: |
|||
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]), |
|||
5: |
|||
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]), |
|||
6: |
|||
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]), |
|||
7: |
|||
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]), |
|||
8: |
|||
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]), |
|||
9: |
|||
dict( |
|||
link=('middle_finger1', 'middle_finger2'), |
|||
id=9, |
|||
color=[102, 178, 255]), |
|||
10: |
|||
dict( |
|||
link=('middle_finger2', 'middle_finger3'), |
|||
id=10, |
|||
color=[102, 178, 255]), |
|||
11: |
|||
dict( |
|||
link=('middle_finger3', 'middle_finger4'), |
|||
id=11, |
|||
color=[102, 178, 255]), |
|||
12: |
|||
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]), |
|||
13: |
|||
dict( |
|||
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]), |
|||
14: |
|||
dict( |
|||
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]), |
|||
15: |
|||
dict( |
|||
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]), |
|||
16: |
|||
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]), |
|||
17: |
|||
dict( |
|||
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]), |
|||
18: |
|||
dict( |
|||
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]), |
|||
19: |
|||
dict( |
|||
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[1.] * 21, |
|||
sigmas=[]) |
|||
@ -0,0 +1,160 @@ |
|||
dataset_info = dict( |
|||
dataset_name='panoptic_pose_3d', |
|||
paper_info=dict( |
|||
author='Joo, Hanbyul and Simon, Tomas and Li, Xulong' |
|||
'and Liu, Hao and Tan, Lei and Gui, Lin and Banerjee, Sean' |
|||
'and Godisart, Timothy and Nabbe, Bart and Matthews, Iain' |
|||
'and Kanade, Takeo and Nobuhara, Shohei and Sheikh, Yaser', |
|||
title='Panoptic Studio: A Massively Multiview System ' |
|||
'for Interaction Motion Capture', |
|||
container='IEEE Transactions on Pattern Analysis' |
|||
' and Machine Intelligence', |
|||
year='2017', |
|||
homepage='http://domedb.perception.cs.cmu.edu', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='neck', id=0, color=[51, 153, 255], type='upper', swap=''), |
|||
1: |
|||
dict(name='nose', id=1, color=[51, 153, 255], type='upper', swap=''), |
|||
2: |
|||
dict(name='mid_hip', id=2, color=[0, 255, 0], type='lower', swap=''), |
|||
3: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=3, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
4: |
|||
dict( |
|||
name='left_elbow', |
|||
id=4, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
5: |
|||
dict( |
|||
name='left_wrist', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
6: |
|||
dict( |
|||
name='left_hip', |
|||
id=6, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
7: |
|||
dict( |
|||
name='left_knee', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
8: |
|||
dict( |
|||
name='left_ankle', |
|||
id=8, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
9: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=9, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
10: |
|||
dict( |
|||
name='right_elbow', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
11: |
|||
dict( |
|||
name='right_wrist', |
|||
id=11, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
12: |
|||
dict( |
|||
name='right_hip', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
13: |
|||
dict( |
|||
name='right_knee', |
|||
id=13, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
14: |
|||
dict( |
|||
name='right_ankle', |
|||
id=14, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle'), |
|||
15: |
|||
dict( |
|||
name='left_eye', |
|||
id=15, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_eye'), |
|||
16: |
|||
dict( |
|||
name='left_ear', |
|||
id=16, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_ear'), |
|||
17: |
|||
dict( |
|||
name='right_eye', |
|||
id=17, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_eye'), |
|||
18: |
|||
dict( |
|||
name='right_ear', |
|||
id=18, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_ear') |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('nose', 'neck'), id=0, color=[51, 153, 255]), |
|||
1: dict(link=('neck', 'left_shoulder'), id=1, color=[0, 255, 0]), |
|||
2: dict(link=('neck', 'right_shoulder'), id=2, color=[255, 128, 0]), |
|||
3: dict(link=('left_shoulder', 'left_elbow'), id=3, color=[0, 255, 0]), |
|||
4: dict( |
|||
link=('right_shoulder', 'right_elbow'), id=4, color=[255, 128, 0]), |
|||
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]), |
|||
6: |
|||
dict(link=('right_elbow', 'right_wrist'), id=6, color=[255, 128, 0]), |
|||
7: dict(link=('left_ankle', 'left_knee'), id=7, color=[0, 255, 0]), |
|||
8: dict(link=('left_knee', 'left_hip'), id=8, color=[0, 255, 0]), |
|||
9: dict(link=('right_ankle', 'right_knee'), id=9, color=[255, 128, 0]), |
|||
10: dict(link=('right_knee', 'right_hip'), id=10, color=[255, 128, 0]), |
|||
11: dict(link=('mid_hip', 'left_hip'), id=11, color=[0, 255, 0]), |
|||
12: dict(link=('mid_hip', 'right_hip'), id=12, color=[255, 128, 0]), |
|||
13: dict(link=('mid_hip', 'neck'), id=13, color=[51, 153, 255]), |
|||
}, |
|||
joint_weights=[ |
|||
1.0, 1.0, 1.0, 1.0, 1.2, 1.5, 1.0, 1.2, 1.5, 1.0, 1.2, 1.5, 1.0, 1.2, |
|||
1.5, 1.0, 1.0, 1.0, 1.0 |
|||
], |
|||
sigmas=[ |
|||
0.026, 0.026, 0.107, 0.079, 0.072, 0.062, 0.107, 0.087, 0.089, 0.079, |
|||
0.072, 0.062, 0.107, 0.087, 0.089, 0.025, 0.035, 0.025, 0.035 |
|||
]) |
|||
@ -0,0 +1,143 @@ |
|||
dataset_info = dict( |
|||
dataset_name='panoptic_hand2d', |
|||
paper_info=dict( |
|||
author='Simon, Tomas and Joo, Hanbyul and ' |
|||
'Matthews, Iain and Sheikh, Yaser', |
|||
title='Hand keypoint detection in single images using ' |
|||
'multiview bootstrapping', |
|||
container='Proceedings of the IEEE conference on ' |
|||
'Computer Vision and Pattern Recognition', |
|||
year='2017', |
|||
homepage='http://domedb.perception.cs.cmu.edu/handdb.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''), |
|||
2: |
|||
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''), |
|||
3: |
|||
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''), |
|||
4: |
|||
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''), |
|||
5: |
|||
dict( |
|||
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''), |
|||
6: |
|||
dict( |
|||
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''), |
|||
7: |
|||
dict( |
|||
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''), |
|||
8: |
|||
dict( |
|||
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''), |
|||
9: |
|||
dict( |
|||
name='middle_finger1', |
|||
id=9, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='middle_finger2', |
|||
id=10, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
11: |
|||
dict( |
|||
name='middle_finger3', |
|||
id=11, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
12: |
|||
dict( |
|||
name='middle_finger4', |
|||
id=12, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
13: |
|||
dict( |
|||
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''), |
|||
14: |
|||
dict( |
|||
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''), |
|||
15: |
|||
dict( |
|||
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''), |
|||
16: |
|||
dict( |
|||
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''), |
|||
17: |
|||
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''), |
|||
18: |
|||
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''), |
|||
19: |
|||
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''), |
|||
20: |
|||
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]), |
|||
5: |
|||
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]), |
|||
6: |
|||
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]), |
|||
7: |
|||
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]), |
|||
8: |
|||
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]), |
|||
9: |
|||
dict( |
|||
link=('middle_finger1', 'middle_finger2'), |
|||
id=9, |
|||
color=[102, 178, 255]), |
|||
10: |
|||
dict( |
|||
link=('middle_finger2', 'middle_finger3'), |
|||
id=10, |
|||
color=[102, 178, 255]), |
|||
11: |
|||
dict( |
|||
link=('middle_finger3', 'middle_finger4'), |
|||
id=11, |
|||
color=[102, 178, 255]), |
|||
12: |
|||
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]), |
|||
13: |
|||
dict( |
|||
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]), |
|||
14: |
|||
dict( |
|||
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]), |
|||
15: |
|||
dict( |
|||
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]), |
|||
16: |
|||
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]), |
|||
17: |
|||
dict( |
|||
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]), |
|||
18: |
|||
dict( |
|||
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]), |
|||
19: |
|||
dict( |
|||
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[1.] * 21, |
|||
sigmas=[]) |
|||
@ -0,0 +1,176 @@ |
|||
dataset_info = dict( |
|||
dataset_name='posetrack18', |
|||
paper_info=dict( |
|||
author='Andriluka, Mykhaylo and Iqbal, Umar and ' |
|||
'Insafutdinov, Eldar and Pishchulin, Leonid and ' |
|||
'Milan, Anton and Gall, Juergen and Schiele, Bernt', |
|||
title='Posetrack: A benchmark for human pose estimation and tracking', |
|||
container='Proceedings of the IEEE Conference on ' |
|||
'Computer Vision and Pattern Recognition', |
|||
year='2018', |
|||
homepage='https://posetrack.net/users/download.php', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''), |
|||
1: |
|||
dict( |
|||
name='head_bottom', |
|||
id=1, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap=''), |
|||
2: |
|||
dict( |
|||
name='head_top', id=2, color=[51, 153, 255], type='upper', |
|||
swap=''), |
|||
3: |
|||
dict( |
|||
name='left_ear', |
|||
id=3, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='right_ear'), |
|||
4: |
|||
dict( |
|||
name='right_ear', |
|||
id=4, |
|||
color=[51, 153, 255], |
|||
type='upper', |
|||
swap='left_ear'), |
|||
5: |
|||
dict( |
|||
name='left_shoulder', |
|||
id=5, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_shoulder'), |
|||
6: |
|||
dict( |
|||
name='right_shoulder', |
|||
id=6, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_shoulder'), |
|||
7: |
|||
dict( |
|||
name='left_elbow', |
|||
id=7, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_elbow'), |
|||
8: |
|||
dict( |
|||
name='right_elbow', |
|||
id=8, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_elbow'), |
|||
9: |
|||
dict( |
|||
name='left_wrist', |
|||
id=9, |
|||
color=[0, 255, 0], |
|||
type='upper', |
|||
swap='right_wrist'), |
|||
10: |
|||
dict( |
|||
name='right_wrist', |
|||
id=10, |
|||
color=[255, 128, 0], |
|||
type='upper', |
|||
swap='left_wrist'), |
|||
11: |
|||
dict( |
|||
name='left_hip', |
|||
id=11, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_hip'), |
|||
12: |
|||
dict( |
|||
name='right_hip', |
|||
id=12, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_hip'), |
|||
13: |
|||
dict( |
|||
name='left_knee', |
|||
id=13, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_knee'), |
|||
14: |
|||
dict( |
|||
name='right_knee', |
|||
id=14, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_knee'), |
|||
15: |
|||
dict( |
|||
name='left_ankle', |
|||
id=15, |
|||
color=[0, 255, 0], |
|||
type='lower', |
|||
swap='right_ankle'), |
|||
16: |
|||
dict( |
|||
name='right_ankle', |
|||
id=16, |
|||
color=[255, 128, 0], |
|||
type='lower', |
|||
swap='left_ankle') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]), |
|||
1: |
|||
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]), |
|||
2: |
|||
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]), |
|||
5: |
|||
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]), |
|||
6: |
|||
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]), |
|||
7: |
|||
dict( |
|||
link=('left_shoulder', 'right_shoulder'), |
|||
id=7, |
|||
color=[51, 153, 255]), |
|||
8: |
|||
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]), |
|||
9: |
|||
dict( |
|||
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]), |
|||
10: |
|||
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]), |
|||
11: |
|||
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]), |
|||
12: |
|||
dict(link=('nose', 'head_bottom'), id=12, color=[51, 153, 255]), |
|||
13: |
|||
dict(link=('nose', 'head_top'), id=13, color=[51, 153, 255]), |
|||
14: |
|||
dict( |
|||
link=('head_bottom', 'left_shoulder'), id=14, color=[51, 153, |
|||
255]), |
|||
15: |
|||
dict( |
|||
link=('head_bottom', 'right_shoulder'), |
|||
id=15, |
|||
color=[51, 153, 255]) |
|||
}, |
|||
joint_weights=[ |
|||
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, |
|||
1.5 |
|||
], |
|||
sigmas=[ |
|||
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062, |
|||
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089 |
|||
]) |
|||
@ -0,0 +1,141 @@ |
|||
dataset_info = dict( |
|||
dataset_name='rhd2d', |
|||
paper_info=dict( |
|||
author='Christian Zimmermann and Thomas Brox', |
|||
title='Learning to Estimate 3D Hand Pose from Single RGB Images', |
|||
container='arXiv', |
|||
year='2017', |
|||
homepage='https://lmb.informatik.uni-freiburg.de/resources/' |
|||
'datasets/RenderedHandposeDataset.en.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''), |
|||
2: |
|||
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''), |
|||
3: |
|||
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''), |
|||
4: |
|||
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''), |
|||
5: |
|||
dict( |
|||
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''), |
|||
6: |
|||
dict( |
|||
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''), |
|||
7: |
|||
dict( |
|||
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''), |
|||
8: |
|||
dict( |
|||
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''), |
|||
9: |
|||
dict( |
|||
name='middle_finger1', |
|||
id=9, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
10: |
|||
dict( |
|||
name='middle_finger2', |
|||
id=10, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
11: |
|||
dict( |
|||
name='middle_finger3', |
|||
id=11, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
12: |
|||
dict( |
|||
name='middle_finger4', |
|||
id=12, |
|||
color=[102, 178, 255], |
|||
type='', |
|||
swap=''), |
|||
13: |
|||
dict( |
|||
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''), |
|||
14: |
|||
dict( |
|||
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''), |
|||
15: |
|||
dict( |
|||
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''), |
|||
16: |
|||
dict( |
|||
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''), |
|||
17: |
|||
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''), |
|||
18: |
|||
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''), |
|||
19: |
|||
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''), |
|||
20: |
|||
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: |
|||
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]), |
|||
1: |
|||
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]), |
|||
2: |
|||
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]), |
|||
3: |
|||
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]), |
|||
4: |
|||
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]), |
|||
5: |
|||
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]), |
|||
6: |
|||
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]), |
|||
7: |
|||
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]), |
|||
8: |
|||
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]), |
|||
9: |
|||
dict( |
|||
link=('middle_finger1', 'middle_finger2'), |
|||
id=9, |
|||
color=[102, 178, 255]), |
|||
10: |
|||
dict( |
|||
link=('middle_finger2', 'middle_finger3'), |
|||
id=10, |
|||
color=[102, 178, 255]), |
|||
11: |
|||
dict( |
|||
link=('middle_finger3', 'middle_finger4'), |
|||
id=11, |
|||
color=[102, 178, 255]), |
|||
12: |
|||
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]), |
|||
13: |
|||
dict( |
|||
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]), |
|||
14: |
|||
dict( |
|||
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]), |
|||
15: |
|||
dict( |
|||
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]), |
|||
16: |
|||
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]), |
|||
17: |
|||
dict( |
|||
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]), |
|||
18: |
|||
dict( |
|||
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]), |
|||
19: |
|||
dict( |
|||
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0]) |
|||
}, |
|||
joint_weights=[1.] * 21, |
|||
sigmas=[]) |
|||
@ -0,0 +1,582 @@ |
|||
dataset_info = dict( |
|||
dataset_name='wflw', |
|||
paper_info=dict( |
|||
author='Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, ' |
|||
'Quan and Cai, Yici and Zhou, Qiang', |
|||
title='Look at boundary: A boundary-aware face alignment algorithm', |
|||
container='Proceedings of the IEEE conference on computer ' |
|||
'vision and pattern recognition', |
|||
year='2018', |
|||
homepage='https://wywu.github.io/projects/LAB/WFLW.html', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict( |
|||
name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-32'), |
|||
1: |
|||
dict( |
|||
name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-31'), |
|||
2: |
|||
dict( |
|||
name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-30'), |
|||
3: |
|||
dict( |
|||
name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-29'), |
|||
4: |
|||
dict( |
|||
name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-28'), |
|||
5: |
|||
dict( |
|||
name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-27'), |
|||
6: |
|||
dict( |
|||
name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-26'), |
|||
7: |
|||
dict( |
|||
name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-25'), |
|||
8: |
|||
dict( |
|||
name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-24'), |
|||
9: |
|||
dict( |
|||
name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-23'), |
|||
10: |
|||
dict( |
|||
name='kpt-10', |
|||
id=10, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-22'), |
|||
11: |
|||
dict( |
|||
name='kpt-11', |
|||
id=11, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-21'), |
|||
12: |
|||
dict( |
|||
name='kpt-12', |
|||
id=12, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-20'), |
|||
13: |
|||
dict( |
|||
name='kpt-13', |
|||
id=13, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-19'), |
|||
14: |
|||
dict( |
|||
name='kpt-14', |
|||
id=14, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-18'), |
|||
15: |
|||
dict( |
|||
name='kpt-15', |
|||
id=15, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-17'), |
|||
16: |
|||
dict(name='kpt-16', id=16, color=[255, 255, 255], type='', swap=''), |
|||
17: |
|||
dict( |
|||
name='kpt-17', |
|||
id=17, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-15'), |
|||
18: |
|||
dict( |
|||
name='kpt-18', |
|||
id=18, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-14'), |
|||
19: |
|||
dict( |
|||
name='kpt-19', |
|||
id=19, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-13'), |
|||
20: |
|||
dict( |
|||
name='kpt-20', |
|||
id=20, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-12'), |
|||
21: |
|||
dict( |
|||
name='kpt-21', |
|||
id=21, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-11'), |
|||
22: |
|||
dict( |
|||
name='kpt-22', |
|||
id=22, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-10'), |
|||
23: |
|||
dict( |
|||
name='kpt-23', id=23, color=[255, 255, 255], type='', |
|||
swap='kpt-9'), |
|||
24: |
|||
dict( |
|||
name='kpt-24', id=24, color=[255, 255, 255], type='', |
|||
swap='kpt-8'), |
|||
25: |
|||
dict( |
|||
name='kpt-25', id=25, color=[255, 255, 255], type='', |
|||
swap='kpt-7'), |
|||
26: |
|||
dict( |
|||
name='kpt-26', id=26, color=[255, 255, 255], type='', |
|||
swap='kpt-6'), |
|||
27: |
|||
dict( |
|||
name='kpt-27', id=27, color=[255, 255, 255], type='', |
|||
swap='kpt-5'), |
|||
28: |
|||
dict( |
|||
name='kpt-28', id=28, color=[255, 255, 255], type='', |
|||
swap='kpt-4'), |
|||
29: |
|||
dict( |
|||
name='kpt-29', id=29, color=[255, 255, 255], type='', |
|||
swap='kpt-3'), |
|||
30: |
|||
dict( |
|||
name='kpt-30', id=30, color=[255, 255, 255], type='', |
|||
swap='kpt-2'), |
|||
31: |
|||
dict( |
|||
name='kpt-31', id=31, color=[255, 255, 255], type='', |
|||
swap='kpt-1'), |
|||
32: |
|||
dict( |
|||
name='kpt-32', id=32, color=[255, 255, 255], type='', |
|||
swap='kpt-0'), |
|||
33: |
|||
dict( |
|||
name='kpt-33', |
|||
id=33, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-46'), |
|||
34: |
|||
dict( |
|||
name='kpt-34', |
|||
id=34, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-45'), |
|||
35: |
|||
dict( |
|||
name='kpt-35', |
|||
id=35, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-44'), |
|||
36: |
|||
dict( |
|||
name='kpt-36', |
|||
id=36, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-43'), |
|||
37: |
|||
dict( |
|||
name='kpt-37', |
|||
id=37, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-42'), |
|||
38: |
|||
dict( |
|||
name='kpt-38', |
|||
id=38, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-50'), |
|||
39: |
|||
dict( |
|||
name='kpt-39', |
|||
id=39, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-49'), |
|||
40: |
|||
dict( |
|||
name='kpt-40', |
|||
id=40, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-48'), |
|||
41: |
|||
dict( |
|||
name='kpt-41', |
|||
id=41, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-47'), |
|||
42: |
|||
dict( |
|||
name='kpt-42', |
|||
id=42, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-37'), |
|||
43: |
|||
dict( |
|||
name='kpt-43', |
|||
id=43, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-36'), |
|||
44: |
|||
dict( |
|||
name='kpt-44', |
|||
id=44, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-35'), |
|||
45: |
|||
dict( |
|||
name='kpt-45', |
|||
id=45, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-34'), |
|||
46: |
|||
dict( |
|||
name='kpt-46', |
|||
id=46, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-33'), |
|||
47: |
|||
dict( |
|||
name='kpt-47', |
|||
id=47, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-41'), |
|||
48: |
|||
dict( |
|||
name='kpt-48', |
|||
id=48, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-40'), |
|||
49: |
|||
dict( |
|||
name='kpt-49', |
|||
id=49, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-39'), |
|||
50: |
|||
dict( |
|||
name='kpt-50', |
|||
id=50, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-38'), |
|||
51: |
|||
dict(name='kpt-51', id=51, color=[255, 255, 255], type='', swap=''), |
|||
52: |
|||
dict(name='kpt-52', id=52, color=[255, 255, 255], type='', swap=''), |
|||
53: |
|||
dict(name='kpt-53', id=53, color=[255, 255, 255], type='', swap=''), |
|||
54: |
|||
dict(name='kpt-54', id=54, color=[255, 255, 255], type='', swap=''), |
|||
55: |
|||
dict( |
|||
name='kpt-55', |
|||
id=55, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-59'), |
|||
56: |
|||
dict( |
|||
name='kpt-56', |
|||
id=56, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-58'), |
|||
57: |
|||
dict(name='kpt-57', id=57, color=[255, 255, 255], type='', swap=''), |
|||
58: |
|||
dict( |
|||
name='kpt-58', |
|||
id=58, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-56'), |
|||
59: |
|||
dict( |
|||
name='kpt-59', |
|||
id=59, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-55'), |
|||
60: |
|||
dict( |
|||
name='kpt-60', |
|||
id=60, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-72'), |
|||
61: |
|||
dict( |
|||
name='kpt-61', |
|||
id=61, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-71'), |
|||
62: |
|||
dict( |
|||
name='kpt-62', |
|||
id=62, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-70'), |
|||
63: |
|||
dict( |
|||
name='kpt-63', |
|||
id=63, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-69'), |
|||
64: |
|||
dict( |
|||
name='kpt-64', |
|||
id=64, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-68'), |
|||
65: |
|||
dict( |
|||
name='kpt-65', |
|||
id=65, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-75'), |
|||
66: |
|||
dict( |
|||
name='kpt-66', |
|||
id=66, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-74'), |
|||
67: |
|||
dict( |
|||
name='kpt-67', |
|||
id=67, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-73'), |
|||
68: |
|||
dict( |
|||
name='kpt-68', |
|||
id=68, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-64'), |
|||
69: |
|||
dict( |
|||
name='kpt-69', |
|||
id=69, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-63'), |
|||
70: |
|||
dict( |
|||
name='kpt-70', |
|||
id=70, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-62'), |
|||
71: |
|||
dict( |
|||
name='kpt-71', |
|||
id=71, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-61'), |
|||
72: |
|||
dict( |
|||
name='kpt-72', |
|||
id=72, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-60'), |
|||
73: |
|||
dict( |
|||
name='kpt-73', |
|||
id=73, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-67'), |
|||
74: |
|||
dict( |
|||
name='kpt-74', |
|||
id=74, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-66'), |
|||
75: |
|||
dict( |
|||
name='kpt-75', |
|||
id=75, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-65'), |
|||
76: |
|||
dict( |
|||
name='kpt-76', |
|||
id=76, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-82'), |
|||
77: |
|||
dict( |
|||
name='kpt-77', |
|||
id=77, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-81'), |
|||
78: |
|||
dict( |
|||
name='kpt-78', |
|||
id=78, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-80'), |
|||
79: |
|||
dict(name='kpt-79', id=79, color=[255, 255, 255], type='', swap=''), |
|||
80: |
|||
dict( |
|||
name='kpt-80', |
|||
id=80, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-78'), |
|||
81: |
|||
dict( |
|||
name='kpt-81', |
|||
id=81, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-77'), |
|||
82: |
|||
dict( |
|||
name='kpt-82', |
|||
id=82, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-76'), |
|||
83: |
|||
dict( |
|||
name='kpt-83', |
|||
id=83, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-87'), |
|||
84: |
|||
dict( |
|||
name='kpt-84', |
|||
id=84, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-86'), |
|||
85: |
|||
dict(name='kpt-85', id=85, color=[255, 255, 255], type='', swap=''), |
|||
86: |
|||
dict( |
|||
name='kpt-86', |
|||
id=86, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-84'), |
|||
87: |
|||
dict( |
|||
name='kpt-87', |
|||
id=87, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-83'), |
|||
88: |
|||
dict( |
|||
name='kpt-88', |
|||
id=88, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-92'), |
|||
89: |
|||
dict( |
|||
name='kpt-89', |
|||
id=89, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-91'), |
|||
90: |
|||
dict(name='kpt-90', id=90, color=[255, 255, 255], type='', swap=''), |
|||
91: |
|||
dict( |
|||
name='kpt-91', |
|||
id=91, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-89'), |
|||
92: |
|||
dict( |
|||
name='kpt-92', |
|||
id=92, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-88'), |
|||
93: |
|||
dict( |
|||
name='kpt-93', |
|||
id=93, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-95'), |
|||
94: |
|||
dict(name='kpt-94', id=94, color=[255, 255, 255], type='', swap=''), |
|||
95: |
|||
dict( |
|||
name='kpt-95', |
|||
id=95, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-93'), |
|||
96: |
|||
dict( |
|||
name='kpt-96', |
|||
id=96, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-97'), |
|||
97: |
|||
dict( |
|||
name='kpt-97', |
|||
id=97, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='kpt-96') |
|||
}, |
|||
skeleton_info={}, |
|||
joint_weights=[1.] * 98, |
|||
sigmas=[]) |
|||
@ -0,0 +1,64 @@ |
|||
dataset_info = dict( |
|||
dataset_name='zebra', |
|||
paper_info=dict( |
|||
author='Graving, Jacob M and Chae, Daniel and Naik, Hemal and ' |
|||
'Li, Liang and Koger, Benjamin and Costelloe, Blair R and ' |
|||
'Couzin, Iain D', |
|||
title='DeepPoseKit, a software toolkit for fast and robust ' |
|||
'animal pose estimation using deep learning', |
|||
container='Elife', |
|||
year='2019', |
|||
homepage='https://github.com/jgraving/DeepPoseKit-Data', |
|||
), |
|||
keypoint_info={ |
|||
0: |
|||
dict(name='snout', id=0, color=[255, 255, 255], type='', swap=''), |
|||
1: |
|||
dict(name='head', id=1, color=[255, 255, 255], type='', swap=''), |
|||
2: |
|||
dict(name='neck', id=2, color=[255, 255, 255], type='', swap=''), |
|||
3: |
|||
dict( |
|||
name='forelegL1', |
|||
id=3, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegR1'), |
|||
4: |
|||
dict( |
|||
name='forelegR1', |
|||
id=4, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='forelegL1'), |
|||
5: |
|||
dict( |
|||
name='hindlegL1', |
|||
id=5, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegR1'), |
|||
6: |
|||
dict( |
|||
name='hindlegR1', |
|||
id=6, |
|||
color=[255, 255, 255], |
|||
type='', |
|||
swap='hindlegL1'), |
|||
7: |
|||
dict(name='tailbase', id=7, color=[255, 255, 255], type='', swap=''), |
|||
8: |
|||
dict(name='tailtip', id=8, color=[255, 255, 255], type='', swap='') |
|||
}, |
|||
skeleton_info={ |
|||
0: dict(link=('head', 'snout'), id=0, color=[255, 255, 255]), |
|||
1: dict(link=('neck', 'head'), id=1, color=[255, 255, 255]), |
|||
2: dict(link=('forelegL1', 'neck'), id=2, color=[255, 255, 255]), |
|||
3: dict(link=('forelegR1', 'neck'), id=3, color=[255, 255, 255]), |
|||
4: dict(link=('hindlegL1', 'tailbase'), id=4, color=[255, 255, 255]), |
|||
5: dict(link=('hindlegR1', 'tailbase'), id=5, color=[255, 255, 255]), |
|||
6: dict(link=('tailbase', 'neck'), id=6, color=[255, 255, 255]), |
|||
7: dict(link=('tailtip', 'tailbase'), id=7, color=[255, 255, 255]) |
|||
}, |
|||
joint_weights=[1.] * 9, |
|||
sigmas=[]) |
|||
@ -0,0 +1,19 @@ |
|||
checkpoint_config = dict(interval=10) |
|||
|
|||
log_config = dict( |
|||
interval=50, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
log_level = 'INFO' |
|||
load_from = None |
|||
resume_from = None |
|||
dist_params = dict(backend='nccl') |
|||
workflow = [('train', 1)] |
|||
|
|||
# disable opencv multithreading to avoid system being overloaded |
|||
opencv_num_threads = 0 |
|||
# set multi-process start method as `fork` to speed up the training |
|||
mp_start_method = 'fork' |
|||
@ -0,0 +1,18 @@ |
|||
# 2D Animal Keypoint Detection |
|||
|
|||
2D animal keypoint detection (animal pose estimation) aims to detect the key-point of different species, including rats, |
|||
dogs, macaques, and cheetah. It provides detailed behavioral analysis for neuroscience, medical and ecology applications. |
|||
|
|||
## Data preparation |
|||
|
|||
Please follow [DATA Preparation](/docs/en/tasks/2d_animal_keypoint.md) to prepare data. |
|||
|
|||
## Demo |
|||
|
|||
Please follow [DEMO](/demo/docs/2d_animal_demo.md) to generate fancy demos. |
|||
|
|||
<img src="https://user-images.githubusercontent.com/11788150/114201893-4446ec00-9989-11eb-808b-5718c47c7b23.gif" height="140px" alt><br> |
|||
|
|||
<img src="https://user-images.githubusercontent.com/11788150/114205282-b5d46980-998c-11eb-9d6b-85ba47f81252.gif" height="140px" alt><br> |
|||
|
|||
<img src="https://user-images.githubusercontent.com/11788150/114023530-944c8280-98a5-11eb-86b0-5f6d3e232af0.gif" height="140px" alt><br> |
|||
@ -0,0 +1,7 @@ |
|||
# Top-down heatmap-based pose estimation |
|||
|
|||
Top-down methods divide the task into two stages: object detection and pose estimation. |
|||
|
|||
They perform object detection first, followed by single-object pose estimation given object bounding boxes. |
|||
Instead of estimating keypoint coordinates directly, the pose estimator will produce heatmaps which represent the |
|||
likelihood of being a keypoint. |
|||
@ -0,0 +1,40 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{sun2019deep, |
|||
title={Deep high-resolution representation learning for human pose estimation}, |
|||
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, |
|||
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, |
|||
pages={5693--5703}, |
|||
year={2019} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ICCV_2019/html/Cao_Cross-Domain_Adaptation_for_Animal_Pose_Estimation_ICCV_2019_paper.html">Animal-Pose (ICCV'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@InProceedings{Cao_2019_ICCV, |
|||
author = {Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing}, |
|||
title = {Cross-Domain Adaptation for Animal Pose Estimation}, |
|||
booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, |
|||
month = {October}, |
|||
year = {2019} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on AnimalPose validation set (1117 instances) |
|||
|
|||
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log | |
|||
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | |
|||
| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w32_animalpose_256x256.py) | 256x256 | 0.736 | 0.959 | 0.832 | 0.775 | 0.966 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256_20210426.log.json) | |
|||
| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w48_animalpose_256x256.py) | 256x256 | 0.737 | 0.959 | 0.823 | 0.778 | 0.962 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_animalpose_256x256-34644726_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_animalpose_256x256_20210426.log.json) | |
|||
@ -0,0 +1,40 @@ |
|||
Collections: |
|||
- Name: HRNet |
|||
Paper: |
|||
Title: Deep high-resolution representation learning for human pose estimation |
|||
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w32_animalpose_256x256.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- HRNet |
|||
Training Data: Animal-Pose |
|||
Name: topdown_heatmap_hrnet_w32_animalpose_256x256 |
|||
Results: |
|||
- Dataset: Animal-Pose |
|||
Metrics: |
|||
AP: 0.736 |
|||
AP@0.5: 0.959 |
|||
AP@0.75: 0.832 |
|||
AR: 0.775 |
|||
AR@0.5: 0.966 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w48_animalpose_256x256.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Animal-Pose |
|||
Name: topdown_heatmap_hrnet_w48_animalpose_256x256 |
|||
Results: |
|||
- Dataset: Animal-Pose |
|||
Metrics: |
|||
AP: 0.737 |
|||
AP@0.5: 0.959 |
|||
AP@0.75: 0.823 |
|||
AR: 0.778 |
|||
AR@0.5: 0.962 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_animalpose_256x256-34644726_20210426.pth |
|||
@ -0,0 +1,172 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/animalpose.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=20, |
|||
dataset_joints=20, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w32-36af842e.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(32, 64)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(32, 64, 128)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(32, 64, 128, 256))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=32, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/animalpose' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_train.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,172 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/animalpose.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=20, |
|||
dataset_joints=20, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w48-8ef0771d.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(48, 96)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(48, 96, 192)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(48, 96, 192, 384))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=48, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/animalpose' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_train.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,141 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/animalpose.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=20, |
|||
dataset_joints=20, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/animalpose' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_train.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,141 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/animalpose.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=20, |
|||
dataset_joints=20, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet152', |
|||
backbone=dict(type='ResNet', depth=152), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/animalpose' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_train.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,141 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/animalpose.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=20, |
|||
dataset_joints=20, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/animalpose' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_train.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalPoseDataset', |
|||
ann_file=f'{data_root}/annotations/animalpose_val.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,41 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{xiao2018simple, |
|||
title={Simple baselines for human pose estimation and tracking}, |
|||
author={Xiao, Bin and Wu, Haiping and Wei, Yichen}, |
|||
booktitle={Proceedings of the European conference on computer vision (ECCV)}, |
|||
pages={466--481}, |
|||
year={2018} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ICCV_2019/html/Cao_Cross-Domain_Adaptation_for_Animal_Pose_Estimation_ICCV_2019_paper.html">Animal-Pose (ICCV'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@InProceedings{Cao_2019_ICCV, |
|||
author = {Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing}, |
|||
title = {Cross-Domain Adaptation for Animal Pose Estimation}, |
|||
booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, |
|||
month = {October}, |
|||
year = {2019} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on AnimalPose validation set (1117 instances) |
|||
|
|||
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log | |
|||
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | |
|||
| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res50_animalpose_256x256.py) | 256x256 | 0.688 | 0.945 | 0.772 | 0.733 | 0.952 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_animalpose_256x256-e1f30bff_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_animalpose_256x256_20210426.log.json) | |
|||
| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res101_animalpose_256x256.py) | 256x256 | 0.696 | 0.948 | 0.785 | 0.737 | 0.954 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_animalpose_256x256-85563f4a_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_animalpose_256x256_20210426.log.json) | |
|||
| [pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res152_animalpose_256x256.py) | 256x256 | 0.709 | 0.948 | 0.797 | 0.749 | 0.951 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_animalpose_256x256-a0a7506c_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_animalpose_256x256_20210426.log.json) | |
|||
@ -0,0 +1,56 @@ |
|||
Collections: |
|||
- Name: SimpleBaseline2D |
|||
Paper: |
|||
Title: Simple baselines for human pose estimation and tracking |
|||
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res50_animalpose_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- SimpleBaseline2D |
|||
Training Data: Animal-Pose |
|||
Name: topdown_heatmap_res50_animalpose_256x256 |
|||
Results: |
|||
- Dataset: Animal-Pose |
|||
Metrics: |
|||
AP: 0.688 |
|||
AP@0.5: 0.945 |
|||
AP@0.75: 0.772 |
|||
AR: 0.733 |
|||
AR@0.5: 0.952 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_animalpose_256x256-e1f30bff_20210426.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res101_animalpose_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Animal-Pose |
|||
Name: topdown_heatmap_res101_animalpose_256x256 |
|||
Results: |
|||
- Dataset: Animal-Pose |
|||
Metrics: |
|||
AP: 0.696 |
|||
AP@0.5: 0.948 |
|||
AP@0.75: 0.785 |
|||
AR: 0.737 |
|||
AR@0.5: 0.954 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_animalpose_256x256-85563f4a_20210426.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res152_animalpose_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Animal-Pose |
|||
Name: topdown_heatmap_res152_animalpose_256x256 |
|||
Results: |
|||
- Dataset: Animal-Pose |
|||
Metrics: |
|||
AP: 0.709 |
|||
AP@0.5: 0.948 |
|||
AP@0.75: 0.797 |
|||
AR: 0.749 |
|||
AR@0.5: 0.951 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_animalpose_256x256-a0a7506c_20210426.pth |
|||
@ -0,0 +1,41 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{sun2019deep, |
|||
title={Deep high-resolution representation learning for human pose estimation}, |
|||
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, |
|||
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, |
|||
pages={5693--5703}, |
|||
year={2019} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://arxiv.org/abs/2108.12617">AP-10K (NeurIPS'2021)</a></summary> |
|||
|
|||
```bibtex |
|||
@misc{yu2021ap10k, |
|||
title={AP-10K: A Benchmark for Animal Pose Estimation in the Wild}, |
|||
author={Hang Yu and Yufei Xu and Jing Zhang and Wei Zhao and Ziyu Guan and Dacheng Tao}, |
|||
year={2021}, |
|||
eprint={2108.12617}, |
|||
archivePrefix={arXiv}, |
|||
primaryClass={cs.CV} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on AP-10K validation set |
|||
|
|||
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AP<sup>M</sup> | AP<sup>L</sup> | ckpt | log | |
|||
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | |
|||
| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w32_ap10k_256x256.py) | 256x256 | 0.738 | 0.958 | 0.808 | 0.592 | 0.743 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_ap10k_256x256-18aac840_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_ap10k_256x256-18aac840_20211029.log.json) | |
|||
| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w48_ap10k_256x256.py) | 256x256 | 0.744 | 0.959 | 0.807 | 0.589 | 0.748 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_ap10k_256x256-d95ab412_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_ap10k_256x256-d95ab412_20211029.log.json) | |
|||
@ -0,0 +1,40 @@ |
|||
Collections: |
|||
- Name: HRNet |
|||
Paper: |
|||
Title: Deep high-resolution representation learning for human pose estimation |
|||
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w32_ap10k_256x256.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- HRNet |
|||
Training Data: AP-10K |
|||
Name: topdown_heatmap_hrnet_w32_ap10k_256x256 |
|||
Results: |
|||
- Dataset: AP-10K |
|||
Metrics: |
|||
AP: 0.738 |
|||
AP@0.5: 0.958 |
|||
AP@0.75: 0.808 |
|||
APL: 0.743 |
|||
APM: 0.592 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_ap10k_256x256-18aac840_20211029.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w48_ap10k_256x256.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: AP-10K |
|||
Name: topdown_heatmap_hrnet_w48_ap10k_256x256 |
|||
Results: |
|||
- Dataset: AP-10K |
|||
Metrics: |
|||
AP: 0.744 |
|||
AP@0.5: 0.959 |
|||
AP@0.75: 0.807 |
|||
APL: 0.748 |
|||
APM: 0.589 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_ap10k_256x256-d95ab412_20211029.pth |
|||
@ -0,0 +1,172 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/ap10k.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=17, |
|||
dataset_joints=17, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w32-36af842e.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(32, 64)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(32, 64, 128)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(32, 64, 128, 256))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=32, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/ap10k' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=4, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,172 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/ap10k.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=17, |
|||
dataset_joints=17, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w48-8ef0771d.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(48, 96)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(48, 96, 192)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(48, 96, 192, 384))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=48, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/ap10k' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=4, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,141 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/ap10k.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=17, |
|||
dataset_joints=17, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/ap10k' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=4, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,141 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/ap10k.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=17, |
|||
dataset_joints=17, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/ap10k' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=4, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalAP10KDataset', |
|||
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
|||
img_prefix=f'{data_root}/data/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,41 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{xiao2018simple, |
|||
title={Simple baselines for human pose estimation and tracking}, |
|||
author={Xiao, Bin and Wu, Haiping and Wei, Yichen}, |
|||
booktitle={Proceedings of the European conference on computer vision (ECCV)}, |
|||
pages={466--481}, |
|||
year={2018} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://arxiv.org/abs/2108.12617">AP-10K (NeurIPS'2021)</a></summary> |
|||
|
|||
```bibtex |
|||
@misc{yu2021ap10k, |
|||
title={AP-10K: A Benchmark for Animal Pose Estimation in the Wild}, |
|||
author={Hang Yu and Yufei Xu and Jing Zhang and Wei Zhao and Ziyu Guan and Dacheng Tao}, |
|||
year={2021}, |
|||
eprint={2108.12617}, |
|||
archivePrefix={arXiv}, |
|||
primaryClass={cs.CV} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on AP-10K validation set |
|||
|
|||
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AP<sup>M</sup> | AP<sup>L</sup> | ckpt | log | |
|||
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | |
|||
| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res50_ap10k_256x256.py) | 256x256 | 0.699 | 0.940 | 0.760 | 0.570 | 0.703 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_ap10k_256x256-35760eb8_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_ap10k_256x256-35760eb8_20211029.log.json) | |
|||
| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res101_ap10k_256x256.py) | 256x256 | 0.698 | 0.943 | 0.754 | 0.543 | 0.702 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_ap10k_256x256-9edfafb9_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_ap10k_256x256-9edfafb9_20211029.log.json) | |
|||
@ -0,0 +1,40 @@ |
|||
Collections: |
|||
- Name: SimpleBaseline2D |
|||
Paper: |
|||
Title: Simple baselines for human pose estimation and tracking |
|||
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res50_ap10k_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- SimpleBaseline2D |
|||
Training Data: AP-10K |
|||
Name: topdown_heatmap_res50_ap10k_256x256 |
|||
Results: |
|||
- Dataset: AP-10K |
|||
Metrics: |
|||
AP: 0.699 |
|||
AP@0.5: 0.94 |
|||
AP@0.75: 0.76 |
|||
APL: 0.703 |
|||
APM: 0.57 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_ap10k_256x256-35760eb8_20211029.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res101_ap10k_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: AP-10K |
|||
Name: topdown_heatmap_res101_ap10k_256x256 |
|||
Results: |
|||
- Dataset: AP-10K |
|||
Metrics: |
|||
AP: 0.698 |
|||
AP@0.5: 0.943 |
|||
AP@0.75: 0.754 |
|||
APL: 0.702 |
|||
APM: 0.543 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_ap10k_256x256-9edfafb9_20211029.pth |
|||
@ -0,0 +1,40 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{sun2019deep, |
|||
title={Deep high-resolution representation learning for human pose estimation}, |
|||
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, |
|||
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, |
|||
pages={5693--5703}, |
|||
year={2019} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://arxiv.org/abs/1906.05586">ATRW (ACM MM'2020)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{li2020atrw, |
|||
title={ATRW: A Benchmark for Amur Tiger Re-identification in the Wild}, |
|||
author={Li, Shuyuan and Li, Jianguo and Tang, Hanlin and Qian, Rui and Lin, Weiyao}, |
|||
booktitle={Proceedings of the 28th ACM International Conference on Multimedia}, |
|||
pages={2590--2598}, |
|||
year={2020} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on ATRW validation set |
|||
|
|||
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log | |
|||
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | |
|||
| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w32_atrw_256x256.py) | 256x256 | 0.912 | 0.973 | 0.959 | 0.938 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_atrw_256x256-f027f09a_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_atrw_256x256_20210414.log.json) | |
|||
| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w48_atrw_256x256.py) | 256x256 | 0.911 | 0.972 | 0.946 | 0.937 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_atrw_256x256-ac088892_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_atrw_256x256_20210414.log.json) | |
|||
@ -0,0 +1,40 @@ |
|||
Collections: |
|||
- Name: HRNet |
|||
Paper: |
|||
Title: Deep high-resolution representation learning for human pose estimation |
|||
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w32_atrw_256x256.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- HRNet |
|||
Training Data: ATRW |
|||
Name: topdown_heatmap_hrnet_w32_atrw_256x256 |
|||
Results: |
|||
- Dataset: ATRW |
|||
Metrics: |
|||
AP: 0.912 |
|||
AP@0.5: 0.973 |
|||
AP@0.75: 0.959 |
|||
AR: 0.938 |
|||
AR@0.5: 0.985 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_atrw_256x256-f027f09a_20210414.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w48_atrw_256x256.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: ATRW |
|||
Name: topdown_heatmap_hrnet_w48_atrw_256x256 |
|||
Results: |
|||
- Dataset: ATRW |
|||
Metrics: |
|||
AP: 0.911 |
|||
AP@0.5: 0.972 |
|||
AP@0.75: 0.946 |
|||
AR: 0.937 |
|||
AR@0.5: 0.985 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_atrw_256x256-ac088892_20210414.pth |
|||
@ -0,0 +1,170 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/atrw.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=15, |
|||
dataset_joints=15, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], |
|||
], |
|||
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w32-36af842e.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(32, 64)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(32, 64, 128)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(32, 64, 128, 256))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=32, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/atrw' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_train.json', |
|||
img_prefix=f'{data_root}/images/train/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,170 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/atrw.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=15, |
|||
dataset_joints=15, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], |
|||
], |
|||
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w48-8ef0771d.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(48, 96)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(48, 96, 192)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(48, 96, 192, 384))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=48, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/atrw' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_train.json', |
|||
img_prefix=f'{data_root}/images/train/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,139 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/atrw.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=15, |
|||
dataset_joints=15, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], |
|||
], |
|||
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/atrw' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_train.json', |
|||
img_prefix=f'{data_root}/images/train/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,139 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/atrw.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=15, |
|||
dataset_joints=15, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], |
|||
], |
|||
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet152', |
|||
backbone=dict(type='ResNet', depth=152), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/atrw' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_train.json', |
|||
img_prefix=f'{data_root}/images/train/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,139 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/atrw.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=15, |
|||
dataset_joints=15, |
|||
dataset_channel=[ |
|||
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], |
|||
], |
|||
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel'], |
|||
soft_nms=False, |
|||
nms_thr=1.0, |
|||
oks_thr=0.9, |
|||
vis_thr=0.2, |
|||
use_gt_bbox=True, |
|||
det_bbox_thr=0.0, |
|||
bbox_file='', |
|||
) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownHalfBodyTransform', |
|||
num_joints_half_body=8, |
|||
prob_half_body=0.3), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/atrw' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_train.json', |
|||
img_prefix=f'{data_root}/images/train/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalATRWDataset', |
|||
ann_file=f'{data_root}/annotations/keypoint_val.json', |
|||
img_prefix=f'{data_root}/images/val/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,41 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{xiao2018simple, |
|||
title={Simple baselines for human pose estimation and tracking}, |
|||
author={Xiao, Bin and Wu, Haiping and Wei, Yichen}, |
|||
booktitle={Proceedings of the European conference on computer vision (ECCV)}, |
|||
pages={466--481}, |
|||
year={2018} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://arxiv.org/abs/1906.05586">ATRW (ACM MM'2020)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{li2020atrw, |
|||
title={ATRW: A Benchmark for Amur Tiger Re-identification in the Wild}, |
|||
author={Li, Shuyuan and Li, Jianguo and Tang, Hanlin and Qian, Rui and Lin, Weiyao}, |
|||
booktitle={Proceedings of the 28th ACM International Conference on Multimedia}, |
|||
pages={2590--2598}, |
|||
year={2020} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on ATRW validation set |
|||
|
|||
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log | |
|||
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: | |
|||
| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res50_atrw_256x256.py) | 256x256 | 0.900 | 0.973 | 0.932 | 0.929 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_atrw_256x256-546c4594_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_atrw_256x256_20210414.log.json) | |
|||
| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res101_atrw_256x256.py) | 256x256 | 0.898 | 0.973 | 0.936 | 0.927 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_atrw_256x256-da93f371_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_atrw_256x256_20210414.log.json) | |
|||
| [pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py) | 256x256 | 0.896 | 0.973 | 0.931 | 0.927 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_atrw_256x256-2bb8e162_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_atrw_256x256_20210414.log.json) | |
|||
@ -0,0 +1,56 @@ |
|||
Collections: |
|||
- Name: SimpleBaseline2D |
|||
Paper: |
|||
Title: Simple baselines for human pose estimation and tracking |
|||
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res50_atrw_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- SimpleBaseline2D |
|||
Training Data: ATRW |
|||
Name: topdown_heatmap_res50_atrw_256x256 |
|||
Results: |
|||
- Dataset: ATRW |
|||
Metrics: |
|||
AP: 0.9 |
|||
AP@0.5: 0.973 |
|||
AP@0.75: 0.932 |
|||
AR: 0.929 |
|||
AR@0.5: 0.985 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_atrw_256x256-546c4594_20210414.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res101_atrw_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: ATRW |
|||
Name: topdown_heatmap_res101_atrw_256x256 |
|||
Results: |
|||
- Dataset: ATRW |
|||
Metrics: |
|||
AP: 0.898 |
|||
AP@0.5: 0.973 |
|||
AP@0.75: 0.936 |
|||
AR: 0.927 |
|||
AR@0.5: 0.985 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_atrw_256x256-da93f371_20210414.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: ATRW |
|||
Name: topdown_heatmap_res152_atrw_256x256 |
|||
Results: |
|||
- Dataset: ATRW |
|||
Metrics: |
|||
AP: 0.896 |
|||
AP@0.5: 0.973 |
|||
AP@0.75: 0.931 |
|||
AR: 0.927 |
|||
AR@0.5: 0.985 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_atrw_256x256-2bb8e162_20210414.pth |
|||
@ -0,0 +1,130 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/fly.py' |
|||
] |
|||
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=32, |
|||
dataset_joints=32, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[192, 192], |
|||
heatmap_size=[48, 48], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/fly' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_train.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,130 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/fly.py' |
|||
] |
|||
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=32, |
|||
dataset_joints=32, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet152', |
|||
backbone=dict(type='ResNet', depth=152), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[192, 192], |
|||
heatmap_size=[48, 48], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/fly' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_train.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,130 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/fly.py' |
|||
] |
|||
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=32, |
|||
dataset_joints=32, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[192, 192], |
|||
heatmap_size=[48, 48], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/fly' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_train.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalFlyDataset', |
|||
ann_file=f'{data_root}/annotations/fly_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,44 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{xiao2018simple, |
|||
title={Simple baselines for human pose estimation and tracking}, |
|||
author={Xiao, Bin and Wu, Haiping and Wei, Yichen}, |
|||
booktitle={Proceedings of the European conference on computer vision (ECCV)}, |
|||
pages={466--481}, |
|||
year={2018} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://www.nature.com/articles/s41592-018-0234-5">Vinegar Fly (Nature Methods'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@article{pereira2019fast, |
|||
title={Fast animal pose estimation using deep neural networks}, |
|||
author={Pereira, Talmo D and Aldarondo, Diego E and Willmore, Lindsay and Kislin, Mikhail and Wang, Samuel S-H and Murthy, Mala and Shaevitz, Joshua W}, |
|||
journal={Nature methods}, |
|||
volume={16}, |
|||
number={1}, |
|||
pages={117--125}, |
|||
year={2019}, |
|||
publisher={Nature Publishing Group} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on Vinegar Fly test set |
|||
|
|||
| Arch | Input Size | PCK@0.2 | AUC | EPE | ckpt | log | |
|||
| :-------- | :--------: | :------: | :------: | :------: |:------: |:------: | |
|||
|[pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res50_fly_192x192.py) | 192x192 | 0.996 | 0.910 | 2.00 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_fly_192x192-5d0ee2d9_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_fly_192x192_20210407.log.json) | |
|||
|[pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res101_fly_192x192.py) | 192x192 | 0.996 | 0.912 | 1.95 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_fly_192x192-41a7a6cc_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_fly_192x192_20210407.log.json) | |
|||
|[pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py) | 192x192 | 0.997 | 0.917 | 1.78 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192_20210407.log.json) | |
|||
@ -0,0 +1,50 @@ |
|||
Collections: |
|||
- Name: SimpleBaseline2D |
|||
Paper: |
|||
Title: Simple baselines for human pose estimation and tracking |
|||
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res50_fly_192x192.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- SimpleBaseline2D |
|||
Training Data: Vinegar Fly |
|||
Name: topdown_heatmap_res50_fly_192x192 |
|||
Results: |
|||
- Dataset: Vinegar Fly |
|||
Metrics: |
|||
AUC: 0.91 |
|||
EPE: 2.0 |
|||
PCK@0.2: 0.996 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_fly_192x192-5d0ee2d9_20210407.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res101_fly_192x192.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Vinegar Fly |
|||
Name: topdown_heatmap_res101_fly_192x192 |
|||
Results: |
|||
- Dataset: Vinegar Fly |
|||
Metrics: |
|||
AUC: 0.912 |
|||
EPE: 1.95 |
|||
PCK@0.2: 0.996 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_fly_192x192-41a7a6cc_20210407.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Vinegar Fly |
|||
Name: topdown_heatmap_res152_fly_192x192 |
|||
Results: |
|||
- Dataset: Vinegar Fly |
|||
Metrics: |
|||
AUC: 0.917 |
|||
EPE: 1.78 |
|||
PCK@0.2: 0.997 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth |
|||
@ -0,0 +1,44 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{sun2019deep, |
|||
title={Deep high-resolution representation learning for human pose estimation}, |
|||
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong}, |
|||
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, |
|||
pages={5693--5703}, |
|||
year={2019} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://openaccess.thecvf.com/content/WACV2021/html/Mathis_Pretraining_Boosts_Out-of-Domain_Robustness_for_Pose_Estimation_WACV_2021_paper.html">Horse-10 (WACV'2021)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{mathis2021pretraining, |
|||
title={Pretraining boosts out-of-domain robustness for pose estimation}, |
|||
author={Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W}, |
|||
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, |
|||
pages={1859--1868}, |
|||
year={2021} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on Horse-10 test set |
|||
|
|||
|Set | Arch | Input Size | PCK@0.3 | NME | ckpt | log | |
|||
| :--- | :---: | :--------: | :------: | :------: |:------: |:------: | |
|||
|split1| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split1.py) | 256x256 | 0.951 | 0.122 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split1-401d901a_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split1_20210405.log.json) | |
|||
|split2| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split2.py) | 256x256 | 0.949 | 0.116 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split2-04840523_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split2_20210405.log.json) | |
|||
|split3| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split3.py) | 256x256 | 0.939 | 0.153 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split3-4db47400_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split3_20210405.log.json) | |
|||
|split1| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split1.py) | 256x256 | 0.973 | 0.095 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split1-3c950d3b_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split1_20210405.log.json) | |
|||
|split2| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split2.py) | 256x256 | 0.969 | 0.101 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split2-8ef72b5d_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split2_20210405.log.json) | |
|||
|split3| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split3.py) | 256x256 | 0.961 | 0.128 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split3-0232ec47_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split3_20210405.log.json) | |
|||
@ -0,0 +1,86 @@ |
|||
Collections: |
|||
- Name: HRNet |
|||
Paper: |
|||
Title: Deep high-resolution representation learning for human pose estimation |
|||
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split1.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- HRNet |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_hrnet_w32_horse10_256x256-split1 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.122 |
|||
PCK@0.3: 0.951 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split1-401d901a_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split2.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_hrnet_w32_horse10_256x256-split2 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.116 |
|||
PCK@0.3: 0.949 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split2-04840523_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split3.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_hrnet_w32_horse10_256x256-split3 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.153 |
|||
PCK@0.3: 0.939 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split3-4db47400_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split1.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_hrnet_w48_horse10_256x256-split1 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.095 |
|||
PCK@0.3: 0.973 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split1-3c950d3b_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split2.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_hrnet_w48_horse10_256x256-split2 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.101 |
|||
PCK@0.3: 0.969 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split2-8ef72b5d_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split3.py |
|||
In Collection: HRNet |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_hrnet_w48_horse10_256x256-split3 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.128 |
|||
PCK@0.3: 0.961 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split3-0232ec47_20210405.pth |
|||
@ -0,0 +1,164 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w32-36af842e.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(32, 64)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(32, 64, 128)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(32, 64, 128, 256))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=32, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,164 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w32-36af842e.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(32, 64)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(32, 64, 128)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(32, 64, 128, 256))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=32, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,164 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w32-36af842e.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(32, 64)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(32, 64, 128)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(32, 64, 128, 256))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=32, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,164 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w48-8ef0771d.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(48, 96)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(48, 96, 192)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(48, 96, 192, 384))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=48, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,164 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w48-8ef0771d.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(48, 96)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(48, 96, 192)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(48, 96, 192, 384))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=48, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,164 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='https://download.openmmlab.com/mmpose/' |
|||
'pretrain_models/hrnet_w48-8ef0771d.pth', |
|||
backbone=dict( |
|||
type='HRNet', |
|||
in_channels=3, |
|||
extra=dict( |
|||
stage1=dict( |
|||
num_modules=1, |
|||
num_branches=1, |
|||
block='BOTTLENECK', |
|||
num_blocks=(4, ), |
|||
num_channels=(64, )), |
|||
stage2=dict( |
|||
num_modules=1, |
|||
num_branches=2, |
|||
block='BASIC', |
|||
num_blocks=(4, 4), |
|||
num_channels=(48, 96)), |
|||
stage3=dict( |
|||
num_modules=4, |
|||
num_branches=3, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4), |
|||
num_channels=(48, 96, 192)), |
|||
stage4=dict( |
|||
num_modules=3, |
|||
num_branches=4, |
|||
block='BASIC', |
|||
num_blocks=(4, 4, 4, 4), |
|||
num_channels=(48, 96, 192, 384))), |
|||
), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=48, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
num_deconv_layers=0, |
|||
extra=dict(final_conv_kernel=1, ), |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet152', |
|||
backbone=dict(type='ResNet', depth=152), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet152', |
|||
backbone=dict(type='ResNet', depth=152), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet152', |
|||
backbone=dict(type='ResNet', depth=152), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split1.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split2.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,133 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/horse10.py' |
|||
] |
|||
evaluation = dict(interval=10, metric='PCK', save_best='PCK') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=22, |
|||
dataset_joints=22, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 21 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
21 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[256, 256], |
|||
heatmap_size=[64, 64], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'bbox_score', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=[ |
|||
'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
|||
'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/horse10' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-train-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalHorse10Dataset', |
|||
ann_file=f'{data_root}/annotations/horse10-test-split3.json', |
|||
img_prefix=f'{data_root}/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,47 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{xiao2018simple, |
|||
title={Simple baselines for human pose estimation and tracking}, |
|||
author={Xiao, Bin and Wu, Haiping and Wei, Yichen}, |
|||
booktitle={Proceedings of the European conference on computer vision (ECCV)}, |
|||
pages={466--481}, |
|||
year={2018} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://openaccess.thecvf.com/content/WACV2021/html/Mathis_Pretraining_Boosts_Out-of-Domain_Robustness_for_Pose_Estimation_WACV_2021_paper.html">Horse-10 (WACV'2021)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{mathis2021pretraining, |
|||
title={Pretraining boosts out-of-domain robustness for pose estimation}, |
|||
author={Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W}, |
|||
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, |
|||
pages={1859--1868}, |
|||
year={2021} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on Horse-10 test set |
|||
|
|||
|Set | Arch | Input Size | PCK@0.3 | NME | ckpt | log | |
|||
| :--- | :---: | :--------: | :------: | :------: |:------: |:------: | |
|||
|split1| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split1.py) | 256x256 | 0.956 | 0.113 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1_20210405.log.json) | |
|||
|split2| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split2.py) | 256x256 | 0.954 | 0.111 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split2-65e2a508_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split2_20210405.log.json) | |
|||
|split3| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split3.py) | 256x256 | 0.946 | 0.129 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split3-9637d4eb_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split3_20210405.log.json) | |
|||
|split1| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split1.py) | 256x256 | 0.958 | 0.115 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split1-1b7c259c_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split1_20210405.log.json) | |
|||
|split2| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split2.py) | 256x256 | 0.955 | 0.115 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split2-30e2fa87_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split2_20210405.log.json) | |
|||
|split3| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split3.py) | 256x256 | 0.946 | 0.126 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split3-2eea5bb1_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split3_20210405.log.json) | |
|||
|split1| [pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split1.py) | 256x256 | 0.969 | 0.105 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split1-7e81fe2d_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split1_20210405.log.json) | |
|||
|split2| [pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split2.py) | 256x256 | 0.970 | 0.103 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split2-3b3404a3_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split2_20210405.log.json) | |
|||
|split3| [pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split3.py) | 256x256 | 0.957 | 0.131 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split3-c957dac5_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split3_20210405.log.json) | |
|||
@ -0,0 +1,125 @@ |
|||
Collections: |
|||
- Name: SimpleBaseline2D |
|||
Paper: |
|||
Title: Simple baselines for human pose estimation and tracking |
|||
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split1.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- SimpleBaseline2D |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res50_horse10_256x256-split1 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.113 |
|||
PCK@0.3: 0.956 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split1-3a3dc37e_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split2.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res50_horse10_256x256-split2 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.111 |
|||
PCK@0.3: 0.954 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split2-65e2a508_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res50_horse10_256x256-split3.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res50_horse10_256x256-split3 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.129 |
|||
PCK@0.3: 0.946 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_horse10_256x256_split3-9637d4eb_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split1.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res101_horse10_256x256-split1 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.115 |
|||
PCK@0.3: 0.958 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split1-1b7c259c_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split2.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res101_horse10_256x256-split2 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.115 |
|||
PCK@0.3: 0.955 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split2-30e2fa87_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split3.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res101_horse10_256x256-split3 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.126 |
|||
PCK@0.3: 0.946 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_horse10_256x256_split3-2eea5bb1_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split1.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res152_horse10_256x256-split1 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.105 |
|||
PCK@0.3: 0.969 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split1-7e81fe2d_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split2.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res152_horse10_256x256-split2 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.103 |
|||
PCK@0.3: 0.97 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split2-3b3404a3_20210405.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split3.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Horse-10 |
|||
Name: topdown_heatmap_res152_horse10_256x256-split3 |
|||
Results: |
|||
- Dataset: Horse-10 |
|||
Metrics: |
|||
NME: 0.131 |
|||
PCK@0.3: 0.957 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_horse10_256x256_split3-c957dac5_20210405.pth |
|||
@ -0,0 +1,130 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/locust.py' |
|||
] |
|||
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=35, |
|||
dataset_joints=35, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet101', |
|||
backbone=dict(type='ResNet', depth=101), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[160, 160], |
|||
heatmap_size=[40, 40], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/locust' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_train.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,130 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/locust.py' |
|||
] |
|||
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=35, |
|||
dataset_joints=35, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet152', |
|||
backbone=dict(type='ResNet', depth=152), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[160, 160], |
|||
heatmap_size=[40, 40], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/locust' |
|||
data = dict( |
|||
samples_per_gpu=32, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_train.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=test_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,130 @@ |
|||
_base_ = [ |
|||
'../../../../_base_/default_runtime.py', |
|||
'../../../../_base_/datasets/locust.py' |
|||
] |
|||
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
|||
|
|||
optimizer = dict( |
|||
type='Adam', |
|||
lr=5e-4, |
|||
) |
|||
optimizer_config = dict(grad_clip=None) |
|||
# learning policy |
|||
lr_config = dict( |
|||
policy='step', |
|||
warmup='linear', |
|||
warmup_iters=500, |
|||
warmup_ratio=0.001, |
|||
step=[170, 200]) |
|||
total_epochs = 210 |
|||
log_config = dict( |
|||
interval=1, |
|||
hooks=[ |
|||
dict(type='TextLoggerHook'), |
|||
# dict(type='TensorboardLoggerHook') |
|||
]) |
|||
|
|||
channel_cfg = dict( |
|||
num_output_channels=35, |
|||
dataset_joints=35, |
|||
dataset_channel=[ |
|||
[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
|||
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 |
|||
], |
|||
], |
|||
inference_channel=[ |
|||
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
|||
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 |
|||
]) |
|||
|
|||
# model settings |
|||
model = dict( |
|||
type='TopDown', |
|||
pretrained='torchvision://resnet50', |
|||
backbone=dict(type='ResNet', depth=50), |
|||
keypoint_head=dict( |
|||
type='TopdownHeatmapSimpleHead', |
|||
in_channels=2048, |
|||
out_channels=channel_cfg['num_output_channels'], |
|||
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
|||
train_cfg=dict(), |
|||
test_cfg=dict( |
|||
flip_test=True, |
|||
post_process='default', |
|||
shift_heatmap=True, |
|||
modulate_kernel=11)) |
|||
|
|||
data_cfg = dict( |
|||
image_size=[160, 160], |
|||
heatmap_size=[40, 40], |
|||
num_output_channels=channel_cfg['num_output_channels'], |
|||
num_joints=channel_cfg['dataset_joints'], |
|||
dataset_channel=channel_cfg['dataset_channel'], |
|||
inference_channel=channel_cfg['inference_channel']) |
|||
|
|||
train_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownRandomFlip', flip_prob=0.5), |
|||
dict( |
|||
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict(type='TopDownGenerateTarget', sigma=2), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img', 'target', 'target_weight'], |
|||
meta_keys=[ |
|||
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
|||
'rotation', 'flip_pairs' |
|||
]), |
|||
] |
|||
|
|||
val_pipeline = [ |
|||
dict(type='LoadImageFromFile'), |
|||
dict(type='TopDownAffine'), |
|||
dict(type='ToTensor'), |
|||
dict( |
|||
type='NormalizeTensor', |
|||
mean=[0.485, 0.456, 0.406], |
|||
std=[0.229, 0.224, 0.225]), |
|||
dict( |
|||
type='Collect', |
|||
keys=['img'], |
|||
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']), |
|||
] |
|||
|
|||
test_pipeline = val_pipeline |
|||
|
|||
data_root = 'data/locust' |
|||
data = dict( |
|||
samples_per_gpu=64, |
|||
workers_per_gpu=2, |
|||
val_dataloader=dict(samples_per_gpu=32), |
|||
test_dataloader=dict(samples_per_gpu=32), |
|||
train=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_train.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=train_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
val=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
test=dict( |
|||
type='AnimalLocustDataset', |
|||
ann_file=f'{data_root}/annotations/locust_test.json', |
|||
img_prefix=f'{data_root}/images/', |
|||
data_cfg=data_cfg, |
|||
pipeline=val_pipeline, |
|||
dataset_info={{_base_.dataset_info}}), |
|||
) |
|||
@ -0,0 +1,43 @@ |
|||
<!-- [ALGORITHM] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary> |
|||
|
|||
```bibtex |
|||
@inproceedings{xiao2018simple, |
|||
title={Simple baselines for human pose estimation and tracking}, |
|||
author={Xiao, Bin and Wu, Haiping and Wei, Yichen}, |
|||
booktitle={Proceedings of the European conference on computer vision (ECCV)}, |
|||
pages={466--481}, |
|||
year={2018} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
<!-- [DATASET] --> |
|||
|
|||
<details> |
|||
<summary align="right"><a href="https://elifesciences.org/articles/47994">Desert Locust (Elife'2019)</a></summary> |
|||
|
|||
```bibtex |
|||
@article{graving2019deepposekit, |
|||
title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning}, |
|||
author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D}, |
|||
journal={Elife}, |
|||
volume={8}, |
|||
pages={e47994}, |
|||
year={2019}, |
|||
publisher={eLife Sciences Publications Limited} |
|||
} |
|||
``` |
|||
|
|||
</details> |
|||
|
|||
Results on Desert Locust test set |
|||
|
|||
| Arch | Input Size | PCK@0.2 | AUC | EPE | ckpt | log | |
|||
| :-------- | :--------: | :------: | :------: | :------: |:------: |:------: | |
|||
|[pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/res50_locust_160x160.py) | 160x160 | 0.999 | 0.899 | 2.27 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_locust_160x160-9efca22b_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_locust_160x160_20210407.log.json) | |
|||
|[pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/res101_locust_160x160.py) | 160x160 | 0.999 | 0.907 | 2.03 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_locust_160x160-d77986b3_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_locust_160x160_20210407.log.json) | |
|||
|[pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/res152_locust_160x160.py) | 160x160 | 1.000 | 0.926 | 1.48 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_locust_160x160-4ea9b372_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_locust_160x160_20210407.log.json) | |
|||
@ -0,0 +1,50 @@ |
|||
Collections: |
|||
- Name: SimpleBaseline2D |
|||
Paper: |
|||
Title: Simple baselines for human pose estimation and tracking |
|||
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html |
|||
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md |
|||
Models: |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/res50_locust_160x160.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: &id001 |
|||
- SimpleBaseline2D |
|||
Training Data: Desert Locust |
|||
Name: topdown_heatmap_res50_locust_160x160 |
|||
Results: |
|||
- Dataset: Desert Locust |
|||
Metrics: |
|||
AUC: 0.899 |
|||
EPE: 2.27 |
|||
PCK@0.2: 0.999 |
|||
Task: Animal 2D Keypoint |
|||
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_locust_160x160-9efca22b_20210407.pth |
|||
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/res101_locust_160x160.py |
|||
In Collection: SimpleBaseline2D |
|||
Metadata: |
|||
Architecture: *id001 |
|||
Training Data: Desert Locust |
|||
Name: topdown_heatmap_res101_locust_160x160 |
|||
Results: |
|||
- Dataset: Desert Locust |
|||
Metrics: |
|||
AUC: 0.907 |
|||
EPE: 2.03 |
|||
PCK@0.2: 0.999 |
|||
Task: Animal 2D Keypoint |
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Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_locust_160x160-d77986b3_20210407.pth |
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- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/res152_locust_160x160.py |
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In Collection: SimpleBaseline2D |
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Metadata: |
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Architecture: *id001 |
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Training Data: Desert Locust |
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Name: topdown_heatmap_res152_locust_160x160 |
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Results: |
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- Dataset: Desert Locust |
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Metrics: |
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AUC: 0.926 |
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EPE: 1.48 |
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PCK@0.2: 1.0 |
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Task: Animal 2D Keypoint |
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Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_locust_160x160-4ea9b372_20210407.pth |
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