This repository contains the source code for our paper: Skeleton-based Self-Supervised Feature Extraction for Improved Dynamic Hand Gesture Recognition
- code will be available soon
Create and activate conda environment:
conda create -n skelmae python=3.10
conda activate skelmae
Install all dependencies:
pip install -r requirements.txt
Dowload the evaluation datasets:
We use MediaPipe framework to extarct hand poses from RGB images (for subsets (training,validation and testing).
- For training set:
python extract_hand_poses.py --data_dir ./Path/to/IPN_Hand/
--annotations_file ./Path/to/IPN_train_annotations.txt
--subset training-set
--save_dir ./datasets/IPN_Hand/Landmarks/
- For testing set
python extract_hand_poses.py --data_dir ./Path/to/IPN_Hand/
--annotations_file ./Path/to/IPN_test_annotations.txt
--subset test-set
--save_dir ./datasets/IPN_Hand/Landmarks/
bash train.sh --config_file configs/ipn_hand_config.yaml
bash eval.sh --config_file configs/ipn_hand_config.yaml
If you find this repo useful, please consider citing our paper
ref
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