Prepare 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:

COCO
COCO-WholeBody
MPII
MPII-TRB
AI Challenger
OCHuman
CrowdPose
sub-JHMDB
OneHand10K
FreiHand
CMU Panoptic HandDB
InterHand2.6M
Human3.6M
MPI-INF-3DHP
LSP
LSPET

COCO

For COCO data, please download from COCO download, 2017 Train/Val is needed for COCO keypoints training and validation. 2014 Train is needed for human mesh estimation training. HRNet-Human-Pose-Estimation provides person detection result of COCO val2017 to reproduce our multi-person pose estimation results. Please download from OneDrive. Download and extract them under $MMPOSE/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── coco
        │-- annotations
        │   │-- person_keypoints_train2017.json
        │   |-- person_keypoints_val2017.json
        |-- person_detection_results
        |   |-- COCO_val2017_detections_AP_H_56_person.json
        │-- train2014
        │   ├── COCO_train2014_000000000009.jpg
        │   ├── COCO_train2014_000000000025.jpg
        │   ├── COCO_train2014_000000000030.jpg
            │-- ...
        │-- train2017
        │   │-- 000000000009.jpg
        │   │-- 000000000025.jpg
        │   │-- 000000000030.jpg
        │   │-- ...
        `-- val2017
            │-- 000000000139.jpg
            │-- 000000000285.jpg
            │-- 000000000632.jpg
            │-- ...

COCO-WholeBody

For COCO-WholeBody datatset, 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. 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 (version>=1.5) to support COCO-WholeBody evaluation:

pip install xtcocotools

MPII

For MPII data, please download from MPII Human Pose Dataset. We have converted the original annotation files into json format, please download them from mpii_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── mpii
        |── annotations
        |   |── mpii_gt_val.mat
        |   |── mpii_test.json
        |   |── mpii_train.json
        |   |── mpii_trainval.json
        |   `── mpii_val.json
        `── images
            |── 000001163.jpg
            |── 000003072.jpg

During training and inference, the prediction result will be saved as '.mat' format by default. We also provide a tool to convert this '.mat' to more readable '.json' format.

python tools/mat2json ${PRED_MAT_FILE} ${GT_JSON_FILE} ${OUTPUT_PRED_JSON_FILE}

For example,

python tools/mat2json work_dirs/res50_mpii_256x256/pred.mat data/mpii/annotations/mpii_val.json pred.json

MPII-TRB

For MPII-TRB data, please download from MPII Human Pose Dataset. Please download the annotation files from mpii_trb_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── mpii
        |── annotations
        |   |── mpii_trb_train.json
        |   |── mpii_trb_val.json
        `── images
            |── 000001163.jpg
            |── 000003072.jpg

AIC

For AIC data, please download from AI Challenger 2017, 2017 Train/Val is needed for keypoints training and validation. Please download the annotation files from aic_annotations. Download and extract them under $MMPOSE/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── aic
        │-- annotations
        │   │-- aic_train.json
        │   |-- aic_val.json
        │-- ai_challenger_keypoint_train_20170902
        │   │-- keypoint_train_images_20170902
        │   │   │-- 0000252aea98840a550dac9a78c476ecb9f47ffa.jpg
        │   │   │-- 000050f770985ac9653198495ef9b5c82435d49c.jpg
        │   │   │-- ...
        `-- ai_challenger_keypoint_validation_20170911
            │-- keypoint_validation_images_20170911
                │-- 0002605c53fb92109a3f2de4fc3ce06425c3b61f.jpg
                │-- 0003b55a2c991223e6d8b4b820045bd49507bf6d.jpg
                │-- ...

CrowdPose

For CrowdPose data, please download from CrowdPose. Please download the annotation files from crowdpose_annotations. For top-down approaches, we follow CrowdPose to use the pre-trained weights of YOLOv3 to generate the detected human bounding boxes. For model training, we follow HigherHRNet to train models on CrowdPose train/val dataset, and evaluate models on CrowdPose test dataset. Download and extract them under $MMPOSE/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── crowdpose
        │-- annotations
        │   │-- mmpose_crowdpose_train.json
        │   │-- mmpose_crowdpose_val.json
        │   │-- mmpose_crowdpose_trainval.json
        │   │-- mmpose_crowdpose_test.json
        │   │-- det_for_crowd_test_0.1_0.5.json
        │-- images
            │-- 100000.jpg
            │-- 100001.jpg
            │-- 100002.jpg
            │-- ...

PoseTrack18

For PoseTrack18 data, please download from PoseTrack18. Please download the annotation files from posetrack18_annotations. We have merged the video-wise separated official annotation files into two json files (posetrack18_train & posetrack18_val.json). We also generate the mask files to speed up training. For top-down approaches, we use MMDetection pre-trained Cascade R-CNN (X-101-64x4d-FPN) to generate the detected human bounding boxes. Please download and extract them under $MMPOSE/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── posetrack18
        │-- annotations
        │   │-- posetrack18_train.json
        │   │-- posetrack18_val.json
        │   │-- posetrack18_val_human_detections.json
        │   │-- train
        │   │   │-- 000001_bonn_train.json
        │   │   │-- 000002_bonn_train.json
        │   │   │-- ...
        │   │-- val
        │   │   │-- 000342_mpii_test.json
        │   │   │-- 000522_mpii_test.json
        │   │   │-- ...
        │   `-- test
        │       │-- 000001_mpiinew_test.json
        │       │-- 000002_mpiinew_test.json
        │       │-- ...
        │
        `-- images
        │   │-- train
        │   │   │-- 000001_bonn_train
        │   │   │   │-- 000000.jpg
        │   │   │   │-- 000001.jpg
        │   │   │   │-- ...
        │   │   │-- ...
        │   │-- val
        │   │   │-- 000342_mpii_test
        │   │   │   │-- 000000.jpg
        │   │   │   │-- 000001.jpg
        │   │   │   │-- ...
        │   │   │-- ...
        │   `-- test
        │       │-- 000001_mpiinew_test
        │       │   │-- 000000.jpg
        │       │   │-- 000001.jpg
        │       │   │-- ...
        │       │-- ...
        `-- mask
            │-- train
            │   │-- 000002_bonn_train
            │   │   │-- 000000.jpg
            │   │   │-- 000001.jpg
            │   │   │-- ...
            │   │-- ...
            `-- val
                │-- 000522_mpii_test
                │   │-- 000000.jpg
                │   │-- 000001.jpg
                │   │-- ...
                │-- ...

The official evaluation tool for PoseTrack should be installed from GitHub.

pip install git+https://github.com/svenkreiss/poseval.git

OCHuman

For OCHuman data, please download the images and annotations from OCHuman, Move them under $MMPOSE/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── ochuman
        │-- annotations
        │   │-- ochuman_coco_format_val_range_0.00_1.00.json
        │   |-- ochuman_coco_format_test_range_0.00_1.00.json
        |-- images
            │-- 000001.jpg
            │-- 000002.jpg
            │-- 000003.jpg
            │-- ...

sub-JHMDB dataset

For sub-JHMDB data, please download the images from JHMDB, Please download the annotation files from jhmdb_annotations. Move them under $MMPOSE/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── jhmdb
        │-- annotations
        │   │-- Sub1_train.json
        │   |-- Sub1_test.json
        │   │-- Sub2_train.json
        │   |-- Sub2_test.json
        │   │-- Sub3_train.json
        │   |-- Sub3_test.json
        |-- Rename_Images
            │-- brush_hair
            │   │--April_09_brush_hair_u_nm_np1_ba_goo_0
            |   │   │--00001.png
            |   │   │--00002.png
            │-- catch
            │-- ...

OneHand10K

For OneHand10K data, please download from OneHand10K Dataset. Please download the annotation files from onehand10k_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── onehand10k
        |── annotations
        |   |── onehand10k_train.json
        |   |── onehand10k_test.json
        `── Train
        |   |── source
        |       |── 0.jpg
        |       |── 1.jpg
        |        ...
        `── Test
            |── source
                |── 0.jpg
                |── 1.jpg

FreiHAND Dataset

For FreiHAND data, please download from FreiHand Dataset. Since the official dataset does not provide validation set, we randomly split the training data into 8:1:1 for train/val/test. Please download the annotation files from freihand_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── onehand10k
        |── annotations
        |   |── freihand_train.json
        |   |── freihand_val.json
        |   |── freihand_test.json
        `── training
            |── rgb
            |   |── 00000000.jpg
            |   |── 00000001.jpg
            |    ...
            |── mask
                |── 00000000.jpg
                |── 00000001.jpg
                 ...

CMU Panoptic HandDB

For CMU Panoptic HandDB, please download from CMU Panoptic HandDB. Following Simon et al, panoptic images (hand143_panopticdb) and MPII & NZSL training sets (manual_train) are used for training, while MPII & NZSL test set (manual_test) for testing. Please download the annotation files from panoptic_annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── panoptic
        |── annotations
        |   |── panoptic_train.json
        |   |── panoptic_test.json
        |
        `── hand143_panopticdb
        |   |── imgs
        |   |   |── 00000000.jpg
        |   |   |── 00000001.jpg
        |   |    ...
        |
        `── hand_labels
            |── manual_train
            |   |── 000015774_01_l.jpg
            |   |── 000015774_01_r.jpg
            |    ...
            |
            `── manual_test
                |── 000648952_02_l.jpg
                |── 000835470_01_l.jpg
                 ...

InterHand2.6M

For InterHand2.6M, please download from InterHand2.6M. Please download the annotation files from annotations. Extract them under {MMPose}/data, and make them look like this:

mmpose
├── mmpose
├── docs
├── tests
├── tools
├── configs
`── data
    │── interhand2.6m
        |── annotations
        |   |── all
        |   |── human_annot
        |   |── machine_annot
        |   |── skeleton.txt
        |   |── subject.txt
        |
        `── images
        |   |── train
        |   |   |-- Capture0 ~ Capture26
        |   |── val
        |   |   |-- Capture0
        |   |── test
        |   |   |-- Capture0 ~ Capture7

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data_preparation.md

data_preparation.md

Prepare datasets

COCO

COCO-WholeBody

MPII

MPII-TRB

AIC

CrowdPose

PoseTrack18

OCHuman

sub-JHMDB dataset

OneHand10K

FreiHAND Dataset

CMU Panoptic HandDB

InterHand2.6M

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data_preparation.md

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data_preparation.md

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Prepare datasets

COCO

COCO-WholeBody

MPII

MPII-TRB

AIC

CrowdPose

PoseTrack18

OCHuman

sub-JHMDB dataset

OneHand10K

FreiHAND Dataset

CMU Panoptic HandDB

InterHand2.6M