# TE-GCN

**Repository Path**: jdlc105/TE-GCN

## Basic Information

- **Project Name**: TE-GCN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-09-29
- **Last Updated**: 2024-10-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TE-GCN

Code for the paper ["Temporal-Enhanced Graph Convolution Network for Skeleton-based Action Recognition"](https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cvi2.12086)


Please cite the following paper if you use this repository in your reseach.
```
@article{xie_tegcn_2022,
author = {Xie, Yulai and Zhang, Yang and Ren, Fang},
doi = {https://doi.org/10.1049/cvi2.12086},
journal = {IET Comput.Vis.},
title = {{Temporal-enhanced graph convolution network for skeleton-based action recognition}},
year = {2022}
}
```

Note that:
- This code is based on [2s-AGCN](https://github.com/lshiwjx/2s-AGCN)

## Data preparation
Prepare the data according to [UAVHuman-Pose processing](https://github.com/xieyulai/UAVHuman_For_TE-GCN)

Your `data/` should be like this:
```
uav
___ xsub1
    ___ test_data.npy
    ___ test_label.pkl
    ___ train_data.npy
    ___ train_label.pkl
___ xsub2
    ___ test_data.npy
    ___ test_label.pkl
    ___ train_data.npy
    ___ train_label.pkl

```

## TRAIN
You can train the your model using the scripts:
```
sh scripts/TRAIN_V1.sh
sh scripts/TRAIN_V2.sh
```

## TEST
You can test the your model using the scripts:
```
sh scripts/EVAL_V1.sh
sh scripts/EVAL_V2.sh
```

## WEIGHTS
We have released two trained weights in [baidupan](https://pan.baidu.com/s/1kourPFzEChrjc8kPO0y6rw),passwd is `nwhu`

Your should put them into `runs/`.

- V1:TOP1-42.37%
- V2:TOP1-68.11%