Quick Run

This page provides a basic tutorial about the usage of MMPose.

We will walk you through the 7 key steps of a typical MMPose workflow by training a top-down residual log-likelihood algorithm based on resnet50 on COCO dataset:

Inference with a pretrained model
Prepare the dataset
Prepare a config
Browse the transformed images
Training
Testing
Visualization

Installation

For installation instructions, please refer to Installation.

Get Started

Inference with a pretrained model

We provide a useful script to perform pose estimation with a pretrained model:

python demo/image_demo.py \
    tests/data/coco/000000000785.jpg \
    configs/body_2d_keypoint/topdown_regression/coco/td-reg_res50_rle-8xb64-210e_coco-256x192.py \
    https://download.openmmlab.com/mmpose/top_down/deeppose/deeppose_res50_coco_256x192_rle-2ea9bb4a_20220616.pth

If MMPose is properly installed, you will get the visualized result as follows:

More demo and full instructions can be found in [Inference](./user_guides/inference.md).

Prepare the dataset

MMPose supports multiple tasks. We provide the corresponding guidelines for data preparation.

You can refer to [2D Body Keypoint Detection] > [COCO] for COCO dataset preparation.

In MMPose, we suggest placing the data under `$MMPOSE/data`.

Prepare a config

MMPose is equipped with a powerful config system to conduct various experiments conveniently. A config file organizes the settings of:

General: basic configurations non-related to training or testing, such as Timer, Logger, Visualizer and other Hooks, as well as distributed-related environment settings
Data: dataset, dataloader and data augmentation
Training: resume, weights loading, optimizer, learning rate scheduling, epochs and valid interval etc.
Model: structure, module and loss function etc.
Evaluation: metrics

We provide a bunch of well-prepared configs under $MMPOSE/configs so that you can directly use or modify.

Going back to our example, we will use the prepared config:

$MMPOSE/configs/body_2d_keypoint/topdown_regression/coco/td-reg_res50_rle-8xb64-210e_coco-256x192.py

You can set the path of the COCO dataset by modifying data_root in the config：

data_root = 'data/coco'

If you wish to learn more about our config system, please refer to [Configs](./user_guides/configs.md).

Browse the transformed images

Before training, we can browse the transformed training data to check if the images are augmented properly:

python tools/misc/browse_dastaset.py \
    configs/body_2d_keypoint/topdown_regression/coco/td-reg_res50_rle-8xb64-210e_coco-256x192.py \
    --mode transformed

Training

Use the following command to train with a single GPU:

python tools/train.py configs/body_2d_keypoint/topdown_regression/coco/td-reg_res50_rle-8xb64-210e_coco-256x192.py

MMPose automates many useful training tricks and functions including:

- Learning rate warmup and scheduling

- ImageNet pretrained models

- Automatic learning rate scaling

- Multi-GPU and Multi-Node training support

- Various Data backend support, e.g. HardDisk, LMDB, Petrel, HTTP etc.

- Mixed precision training support

- TensorBoard

Testing

Checkpoints and logs will be saved under $MMPOSE/work_dirs by default. The best model is under $MMPOSE/work_dir/best_coco.

Use the following command to evaluate the model on COCO dataset:

python tools/test.py \
    configs/body_2d_keypoint/topdown_regression/coco/td-reg_res50_rle-8xb64-210e_coco-256x192.py \
    work_dir/best_coco/AP_epoch_20.pth

Here is an example of evaluation results：

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.704
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.883
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.777
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.667
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.769
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] =  0.751
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] =  0.920
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] =  0.815
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] =  0.709
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] =  0.811
08/23 12:04:42 - mmengine - INFO - Epoch(test) [3254/3254]  coco/AP: 0.704168  coco/AP .5: 0.883134  coco/AP .75: 0.777015  coco/AP (M): 0.667207  coco/AP (L): 0.768644  coco/AR: 0.750913  coco/AR .5: 0.919710  coco/AR .75: 0.815334  coco/AR (M): 0.709232  coco/AR (L): 0.811334

If you want to perform evaluation on other datasets, please refer to [Train & Test](./user_guides/train_and_test.md).

Visualization

In addition to the visualization of the keypoint skeleton, MMPose also supports the visualization of Heatmaps by setting output_heatmap=True in confg:

model = dict(
    ## omitted
    test_cfg = dict(
        ## omitted
        output_heatmaps=True
    )
)

or add --cfg-options='model.test_cfg.output_heatmaps=True' at the end of your command.

Visualization result (top: decoded keypoints; bottom: predicted heatmap):

If you wish to apply MMPose to your own projects, we have prepared a detailed [Migration guide](./migration.md).

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