# 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](#300w-dataset) \[ [Homepage](https://ibug.doc.ic.ac.uk/resources/300-W/) \] - [WFLW](#wflw-dataset) \[ [Homepage](https://wywu.github.io/projects/LAB/WFLW.html) \] - [AFLW](#aflw-dataset) \[ [Homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/) \] - [COFW](#cofw-dataset) \[ [Homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/) \] - [COCO-WholeBody-Face](#coco-wholebody-face) \[ [Homepage](https://github.com/jin-s13/COCO-WholeBody/) \] ## 300W Dataset

```bibtex @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](https://ibug.doc.ic.ac.uk/resources/300-W/). Please download the annotation files from [300w_annotations](https://download.openmmlab.com/mmpose/datasets/300w_annotations.tar). Extract them under {MMPose}/data, and make them look like this: ```text 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)

```bibtex @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](https://wywu.github.io/projects/LAB/WFLW.html). Please download the annotation files from [wflw_annotations](https://download.openmmlab.com/mmpose/datasets/wflw_annotations.tar). Extract them under {MMPose}/data, and make them look like this: ```text 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)

```bibtex @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](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/). Please download the annotation files from [aflw_annotations](https://download.openmmlab.com/mmpose/datasets/aflw_annotations.tar). Extract them under {MMPose}/data, and make them look like this: ```text 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)

```bibtex @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)](http://www.vision.caltech.edu/xpburgos/ICCV13/Data/COFW_color.zip). Move `COFW_train_color.mat` and `COFW_test_color.mat` to `data/cofw/` and make them look like: ```text 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 ```text 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)

```bibtex @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](https://github.com/jin-s13/COCO-WholeBody/) dataset, images can be downloaded from [COCO download](http://cocodataset.org/#download), 2017 Train/Val is needed for COCO keypoints training and validation. Download COCO-WholeBody annotations for COCO-WholeBody annotations for [Train](https://drive.google.com/file/d/1thErEToRbmM9uLNi1JXXfOsaS5VK2FXf/view?usp=sharing) / [Validation](https://drive.google.com/file/d/1N6VgwKnj8DeyGXCvp1eYgNbRmw6jdfrb/view?usp=sharing) (Google Drive). Download person detection result of COCO val2017 from [OneDrive](https://1drv.ms/f/s!AhIXJn_J-blWzzDXoz5BeFl8sWM-) or [GoogleDrive](https://drive.google.com/drive/folders/1fRUDNUDxe9fjqcRZ2bnF_TKMlO0nB_dk?usp=sharing). Download and extract them under $MMPOSE/data, and make them look like this: ```text 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](https://github.com/jin-s13/xtcocoapi) to support COCO-WholeBody evaluation: `pip install xtcocotools`