forked from open-mmlab/mmsegmentation
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Docs] Refine docs (open-mmlab#1656)
- Loading branch information
Showing
46 changed files
with
1,496 additions
and
148 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,55 @@ | ||
| <!-- [ALGORITHM] --> | ||
|
|
||
| <details> | ||
| <summary align="right"><a href="https://arxiv.org/abs/2107.03332">SimCC (ECCV'2022)</a></summary> | ||
|
|
||
| ```bibtex | ||
| @misc{https://doi.org/10.48550/arxiv.2107.03332, | ||
| title={SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation}, | ||
| author={Li, Yanjie and Yang, Sen and Liu, Peidong and Zhang, Shoukui and Wang, Yunxiao and Wang, Zhicheng and Yang, Wankou and Xia, Shu-Tao}, | ||
| year={2021} | ||
| } | ||
| ``` | ||
|
|
||
| </details> | ||
|
|
||
| <!-- [BACKBONE] --> | ||
|
|
||
| <details> | ||
| <summary align="right"><a href="http://openaccess.thecvf.com/content_cvpr_2018/html/Sandler_MobileNetV2_Inverted_Residuals_CVPR_2018_paper.html">MobilenetV2 (CVPR'2018)</a></summary> | ||
|
|
||
| ```bibtex | ||
| @inproceedings{sandler2018mobilenetv2, | ||
| title={Mobilenetv2: Inverted residuals and linear bottlenecks}, | ||
| author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh}, | ||
| booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, | ||
| pages={4510--4520}, | ||
| year={2018} | ||
| } | ||
| ``` | ||
|
|
||
| </details> | ||
|
|
||
| <!-- [DATASET] --> | ||
|
|
||
| <details> | ||
| <summary align="right"><a href="https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48">COCO (ECCV'2014)</a></summary> | ||
|
|
||
| ```bibtex | ||
| @inproceedings{lin2014microsoft, | ||
| title={Microsoft coco: Common objects in context}, | ||
| author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, | ||
| booktitle={European conference on computer vision}, | ||
| pages={740--755}, | ||
| year={2014}, | ||
| organization={Springer} | ||
| } | ||
| ``` | ||
|
|
||
| </details> | ||
|
|
||
| Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset | ||
|
|
||
| | Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log | | ||
| | :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: | | ||
| | [simcc_mobilenetv2_wo_deconv](/configs/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192.py) | 256x192 | 0.620 | 0.855 | 0.697 | 0.678 | 0.902 | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192-e0cc028d_20220922.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192-e0cc028d_20220922.log.json) | |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
127 changes: 127 additions & 0 deletions
127
configs/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| _base_ = ['../../../_base_/default_runtime.py'] | ||
|
|
||
| # runtime | ||
| train_cfg = dict(max_epochs=210, val_interval=10) | ||
|
|
||
| # optimizer | ||
| optim_wrapper = dict(optimizer=dict( | ||
| type='Adam', | ||
| lr=5e-4, | ||
| )) | ||
|
|
||
| # learning policy | ||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', begin=0, end=500, start_factor=0.001, | ||
| by_epoch=False), # warm-up | ||
| dict( | ||
| type='MultiStepLR', | ||
| begin=0, | ||
| end=train_cfg['max_epochs'], | ||
| milestones=[170, 200], | ||
| gamma=0.1, | ||
| by_epoch=True) | ||
| ] | ||
|
|
||
| # automatically scaling LR based on the actual training batch size | ||
| auto_scale_lr = dict(base_batch_size=512) | ||
|
|
||
| # codec settings | ||
| codec = dict( | ||
| type='SimCCLabel', input_size=(192, 256), sigma=6.0, simcc_split_ratio=2.0) | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='TopdownPoseEstimator', | ||
| data_preprocessor=dict( | ||
| type='PoseDataPreprocessor', | ||
| mean=[123.675, 116.28, 103.53], | ||
| std=[58.395, 57.12, 57.375], | ||
| bgr_to_rgb=True), | ||
| backbone=dict( | ||
| type='MobileNetV2', | ||
| widen_factor=1., | ||
| out_indices=(7, ), | ||
| init_cfg=dict( | ||
| type='Pretrained', | ||
| checkpoint='mmcls://mobilenet_v2', | ||
| )), | ||
| head=dict( | ||
| type='SimCCHead', | ||
| in_channels=1280, | ||
| out_channels=17, | ||
| input_size=codec['input_size'], | ||
| in_featuremap_size=(6, 8), | ||
| simcc_split_ratio=codec['simcc_split_ratio'], | ||
| deconv_out_channels=None, | ||
| loss=dict(type='KLDiscretLoss', use_target_weight=True), | ||
| decoder=codec), | ||
| test_cfg=dict(flip_test=True, )) | ||
|
|
||
| # base dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_mode = 'topdown' | ||
| data_root = 'data/coco/' | ||
|
|
||
| file_client_args = dict(backend='disk') | ||
|
|
||
| # pipelines | ||
| train_pipeline = [ | ||
| dict(type='LoadImage', file_client_args=file_client_args), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='RandomFlip', direction='horizontal'), | ||
| dict(type='RandomHalfBody'), | ||
| dict(type='RandomBBoxTransform'), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict( | ||
| type='GenerateTarget', target_type='keypoint_xy_label', encoder=codec), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
| val_pipeline = [ | ||
| dict(type='LoadImage', file_client_args=file_client_args), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
|
|
||
| # data loaders | ||
| train_dataloader = dict( | ||
| batch_size=64, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| data_mode=data_mode, | ||
| ann_file='annotations/person_keypoints_train2017.json', | ||
| data_prefix=dict(img='train2017/'), | ||
| pipeline=train_pipeline, | ||
| )) | ||
| val_dataloader = dict( | ||
| batch_size=32, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| data_mode=data_mode, | ||
| ann_file='annotations/person_keypoints_val2017.json', | ||
| bbox_file=f'{data_root}person_detection_results/' | ||
| 'COCO_val2017_detections_AP_H_56_person.json', | ||
| data_prefix=dict(img='val2017/'), | ||
| test_mode=True, | ||
| pipeline=val_pipeline, | ||
| )) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| # hooks | ||
| default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) | ||
|
|
||
| # evaluators | ||
| val_evaluator = dict( | ||
| type='CocoMetric', | ||
| ann_file=data_root + 'annotations/person_keypoints_val2017.json') | ||
| test_evaluator = val_evaluator |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.