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Release GaitLU-1M

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datasets/GaitLU-1M/README.md
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@@ -6,14 +6,23 @@ GaitLU-1M is extracted from public videos shot around the world, making it cover
This great diversity and scale offer an excellent chance to learn general gait representation in a self-supervised manner.
## Download (Coming soon)
Download the dataset from [Baidu Yun]() or [OneDrive]() and decompress the file by following command:
```shell
unzip -P password GaitLU-1M.zip | xargs -n1 tar xzvf
```
To obtain the password, you should sign the [Release Agreement](./Release_Agreement.pdf) and [Ethical Requirement](./Ethical_Requirements.pdf) and send the signed documents to the administrator(12131100@mail.sustech.edu.cn).
## Download
### Step1:
Download the dataset from [Baidu Yun](https://pan.baidu.com/s/1aexoZY-deZFXSuyfOOjwJg) (code: 4rat) or [OneDrive](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/noahshen_connect_hku_hk/EvFZrNKeV7VCgJKCaLay7T8Bv7TW5PHIcXWzv0XyFPliIA?e=9ZHkx9).
Then you can get GaitLU-1M formatted as:
There are 6 sub-zip files and you can aggregate them by:
```shell
zip -F GaitLU_Anno_part.zip --out GaitLU_Anno.zip
```
### Step2:
Then, you can decompress the file by:
```shell
unzip -P password GaitLU_Anno.zip -d <output_folder>
```
To obtain the password, you should sign the [Release Agreement](./Release_Agreement.pdf) and [Ethical Requirement](./Ethical_Requirements.pdf) and send the signed documents to our administrator (12131100@mail.sustech.edu.cn).
Finally, you can get GaitLU-1M formatted as:
```
silhouette_cut_pkl
├── 000 # Random number
@@ -27,12 +36,12 @@ silhouette_cut_pkl
```
## Usage (Coming soon)
## Usage
For the training phase, you should modify the `dataset_root` in `configs/gaitssb/pretrain.yaml` and run the following command:
```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/gaitssb/pretrain.yaml --phase train --log_to_file
```
The officially provided pretrained checkpoint can be found [here]().
The officially provided pretrained checkpoint can be found [here]() (Coming soon).
Then you can evaluate the pretrained model on labelled gait datasets by runing:
```shell
@@ -60,12 +69,12 @@ If you use this dataset in your research, please cite the following paper:
```
If you think OpenGait is useful, please cite the following paper:
```
@misc{fan2022opengait,
title={OpenGait: Revisiting Gait Recognition Toward Better Practicality},
author={Chao Fan and Junhao Liang and Chuanfu Shen and Saihui Hou and Yongzhen Huang and Shiqi Yu},
year={2022},
eprint={2211.06597},
archivePrefix={arXiv},
primaryClass={cs.CV}
@InProceedings{Fan_2023_CVPR,
author = {Fan, Chao and Liang, Junhao and Shen, Chuanfu and Hou, Saihui and Huang, Yongzhen and Yu, Shiqi},
title = {OpenGait: Revisiting Gait Recognition Towards Better Practicality},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {9707-9716}
}
```