SCAPE [ECCV 2024]

Official code repository for the paper:
[SCAPE: A Simple and Strong Category-Agnostic Pose Estimator]

TODO:

• Release code for train and inference
• Release pre-trained models

Usage

Install

We train and evaluate our model on Python 3.7 and Pytorch 1.10.2 with CUDA 11.1. Other versions can probably work. Please first install pytorch and torchvision following official documentation Pytorch. Then, prepare the following packages:

mmcv-full=1.3.17
mmpose=1.3.17

Having installed these packages, run python setup.py develop.

Data preparation

Please follow the official guide to prepare the MP-100 dataset for training and evaluation, and organize the data structure properly.

Training

Training CAPEFormer on 1-shot setting only need one GPU (8GB memory is enough) and 5-shot setting only need one GPU(>12GB). To train the model, first specify the data path and output directory in the configuration file. Here we show an example of training CAPEFormer on MP-100 dataset split1.

Train with a single GPU

python train.py --config ${CONFIG_FILE} --work-dir ${SAVE_PATH} [optional arguments]

Train with multiple GPUs with pytorch distributed backend

Please follow the official guide

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} ${SAVE_PATH}

Test

Test with a single GPU

The evaluation on a single GPU will cost approximatly 40min. After organizing the data and the pre-trained checkpoints, you can run the following commands for testing:

python test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Citation

Acknowledgement

Thanks to:

• MMPose
• Pose-for-Everything
• CapeFormer

License

This project is released under the Apache 2.0 license.