2D Face Keypoint Datasets

It is recommended to symlink the dataset root to $MMPOSE/data. If your folder structure is different, you may need to change the corresponding paths in config files.

MMPose supported datasets:

300W Dataset

300W (IMAVIS'2016)

@article{sagonas2016300,
  title={300 faces in-the-wild challenge: Database and results},
  author={Sagonas, Christos and Antonakos, Epameinondas and Tzimiropoulos, Georgios and Zafeiriou, Stefanos and Pantic, Maja},
  journal={Image and vision computing},
  volume={47},
  pages={3--18},
  year={2016},
  publisher={Elsevier}
}

For 300W data, please download images from 300W Dataset. Please download the annotation files from 300w_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── 300w
        |── annotations
        |   |── face_landmarks_300w_train.json
        |   |── face_landmarks_300w_valid.json
        |   |── face_landmarks_300w_valid_common.json
        |   |── face_landmarks_300w_valid_challenge.json
        |   |── face_landmarks_300w_test.json
        `── images
            |── afw
            |   |── 1051618982_1.jpg
            |   |── 111076519_1.jpg
            |    ...
            |── helen
            |   |── trainset
            |   |   |── 100032540_1.jpg
            |   |   |── 100040721_1.jpg
            |   |    ...
            |   |── testset
            |   |   |── 296814969_3.jpg
            |   |   |── 2968560214_1.jpg
            |   |    ...
            |── ibug
            |   |── image_003_1.jpg
            |   |── image_004_1.jpg
            |    ...
            |── lfpw
            |   |── trainset
            |   |   |── image_0001.png
            |   |   |── image_0002.png
            |   |    ...
            |   |── testset
            |   |   |── image_0001.png
            |   |   |── image_0002.png
            |   |    ...
            `── Test
                |── 01_Indoor
                |   |── indoor_001.png
                |   |── indoor_002.png
                |    ...
                `── 02_Outdoor
                    |── outdoor_001.png
                    |── outdoor_002.png
                     ...

WFLW Dataset

WFLW (CVPR'2018)

@inproceedings{wu2018look,
  title={Look at boundary: A boundary-aware face alignment algorithm},
  author={Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, Quan and Cai, Yici and Zhou, Qiang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2129--2138},
  year={2018}
}

For WFLW data, please download images from WFLW Dataset. Please download the annotation files from wflw_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── wflw
        |── annotations
        |   |── face_landmarks_wflw_train.json
        |   |── face_landmarks_wflw_test.json
        |   |── face_landmarks_wflw_test_blur.json
        |   |── face_landmarks_wflw_test_occlusion.json
        |   |── face_landmarks_wflw_test_expression.json
        |   |── face_landmarks_wflw_test_largepose.json
        |   |── face_landmarks_wflw_test_illumination.json
        |   |── face_landmarks_wflw_test_makeup.json
        |
        `── images
            |── 0--Parade
            |   |── 0_Parade_marchingband_1_1015.jpg
            |   |── 0_Parade_marchingband_1_1031.jpg
            |    ...
            |── 1--Handshaking
            |   |── 1_Handshaking_Handshaking_1_105.jpg
            |   |── 1_Handshaking_Handshaking_1_107.jpg
            |    ...
            ...

AFLW Dataset

AFLW (ICCVW'2011)

@inproceedings{koestinger2011annotated,
  title={Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization},
  author={Koestinger, Martin and Wohlhart, Paul and Roth, Peter M and Bischof, Horst},
  booktitle={2011 IEEE international conference on computer vision workshops (ICCV workshops)},
  pages={2144--2151},
  year={2011},
  organization={IEEE}
}

For AFLW data, please download images from AFLW Dataset. Please download the annotation files from aflw_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── aflw
        |── annotations
        |   |── face_landmarks_aflw_train.json
        |   |── face_landmarks_aflw_test_frontal.json
        |   |── face_landmarks_aflw_test.json
        `── images
            |── flickr
                |── 0
                |   |── image00002.jpg
                |   |── image00013.jpg
                |    ...
                |── 2
                |   |── image00004.jpg
                |   |── image00006.jpg
                |    ...
                `── 3
                    |── image00032.jpg
                    |── image00035.jpg
                     ...

COFW Dataset

COFW (ICCV'2013)

@inproceedings{burgos2013robust,
  title={Robust face landmark estimation under occlusion},
  author={Burgos-Artizzu, Xavier P and Perona, Pietro and Doll{\'a}r, Piotr},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={1513--1520},
  year={2013}
}

For COFW data, please download from COFW Dataset (Color Images). Move COFW_train_color.mat and COFW_test_color.mat to data/cofw/ and make them look like:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── cofw
        |── COFW_train_color.mat
        |── COFW_test_color.mat

Run the following script under {MMPose}/data

python tools/dataset_converters/parse_cofw_dataset.py

And you will get

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── cofw
        |── COFW_train_color.mat
        |── COFW_test_color.mat
        |── annotations
        |   |── cofw_train.json
        |   |── cofw_test.json
        |── images
            |── 000001.jpg
            |── 000002.jpg

COCO-WholeBody (Face)

COCO-WholeBody-Face (ECCV'2020)

@inproceedings{jin2020whole,
  title={Whole-Body Human Pose Estimation in the Wild},
  author={Jin, Sheng and Xu, Lumin and Xu, Jin and Wang, Can and Liu, Wentao and Qian, Chen and Ouyang, Wanli and Luo, Ping},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  year={2020}
}

For COCO-WholeBody dataset, images can be downloaded from COCO download, 2017 Train/Val is needed for COCO keypoints training and validation. Download COCO-WholeBody annotations for COCO-WholeBody annotations for Train / Validation (Google Drive). Download person detection result of COCO val2017 from OneDrive or GoogleDrive. Download and extract them under $MMPOSE/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── coco
        │-- annotations
        │   │-- coco_wholebody_train_v1.0.json
        │   |-- coco_wholebody_val_v1.0.json
        |-- person_detection_results
        |   |-- COCO_val2017_detections_AP_H_56_person.json
        │-- train2017
        │   │-- 000000000009.jpg
        │   │-- 000000000025.jpg
        │   │-- 000000000030.jpg
        │   │-- ...
        `-- val2017
            │-- 000000000139.jpg
            │-- 000000000285.jpg
            │-- 000000000632.jpg
            │-- ...

Please also install the latest version of Extended COCO API to support COCO-WholeBody evaluation:

pip install xtcocotools

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