https://github.com/iliasprc/slrzoo
https://github.com/iliasprc/slrzoo
computer-vision continuous-sign-language deep-learning isolated-sign-language-recognition sign-language-recognition signlanguagerecognition slr
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/iliasprc/slrzoo
- Owner: iliasprc
- License: mit
- Created: 2023-09-20T09:37:28.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-20T13:45:37.000Z (almost 3 years ago)
- Last Synced: 2025-04-14T00:34:06.839Z (about 1 year ago)
- Topics: computer-vision, continuous-sign-language, deep-learning, isolated-sign-language-recognition, sign-language-recognition, signlanguagerecognition, slr
- Language: Python
- Homepage:
- Size: 44.2 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# SLRZoo: Sign Language Recognition with deep learning methods
[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
## Usage
### Requirements
Create a new virtual environment
```
conda create -n slrzooenv
conda activate slrzooenv
```
and install requirements:
```setup
pip install -r requirements.txt
```
## Training
To train the model(s) for Continuous Sign Language Recognition run this command:
```train
python train_cslr.py
```
To train the model(s) for Isolated Sign Language Recognition run this command:
```train
python train_islr.py
```
## Evaluation
To evaluate my model on ImageNet, run:
```eval
python eval.py --pretrained-cpkt mymodel.pth
```
### Download
You can run [download.sh](download.sh) which automatically downloads datasets (except CSL-Daily, whose downloading needs an agreement submission), pretrained models, keypoints and place them under corresponding locations. Or you can download these files separately as follows.
**Datasets**
Download datasets from their websites and place them under the corresponding directories in data/
* [Phoenix-2014](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX/)
* [Phoenix-2014T](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/)
* [CSL-Daily](http://home.ustc.edu.cn/~zhouh156/dataset/csl-daily/)
* [CSL-500](https://ustc-slr.github.io/datasets/2015_csl/)
* [GSL](https://zenodo.org/record/3941811)
* [MS-ASL](https://microsoft.github.io/data-for-society/dataset?d=MS-ASL-American-Sign-Language-Dataset)
### Implemented methods
1. Camgoz, N. C., Hadfield, S., Koller, O., & Bowden, R. (2017, October). Subunets: End-to-end hand shape and continuous sign language recognition. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 3075-3084). IEEE.
1. Cui, Runpeng, Hu Liu, and Changshui Zhang. "A deep neural framework for continuous sign language recognition by iterative training." IEEE Transactions on Multimedia 21.7 (2019): 1880-1891.
1. H. Zhou, W. Zhou and H. Li, "Dynamic Pseudo Label Decoding for Continuous Sign Language Recognition," 2019 IEEE International Conference on Multimedia and Expo (ICME), Shanghai, China, 2019, pp. 1282-1287.
1. Pu, Junfu, Wengang Zhou, and Houqiang Li. "Iterative alignment network for continuous sign language recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
[contributors-shield]: https://img.shields.io/github/contributors/iliasprc/slrzoo.svg?style=flat-square
[contributors-url]: https://github.com/iliasprc/slrzoo/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/iliasprc/slrzoo.svg?style=flat-square
[forks-url]: https://github.com/iliasprc/slrzoo/network/members
[stars-shield]: https://img.shields.io/github/stars/iliasprc/slrzoo.svg?style=flat-square
[stars-url]: https://github.com/iliasprc/slrzoo/stargazers
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[issues-url]: https://github.com/iliasprc/slrzoo/issues