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https://github.com/luluw8071/asl-alphabet-detection
American Sign Language Alphabet Detection in Real Time using OpenCV-Mediapipe with EfficientNetB0 in PyTorch
https://github.com/luluw8071/asl-alphabet-detection
american-sign-language deep-neural-networks efficientnetb0 image-classification python pytorch sign-language-recognition streamlit
Last synced: about 2 months ago
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American Sign Language Alphabet Detection in Real Time using OpenCV-Mediapipe with EfficientNetB0 in PyTorch
- Host: GitHub
- URL: https://github.com/luluw8071/asl-alphabet-detection
- Owner: LuluW8071
- License: mit
- Created: 2024-03-08T15:49:50.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-09-24T17:51:51.000Z (3 months ago)
- Last Synced: 2024-11-12T06:09:26.693Z (about 2 months ago)
- Topics: american-sign-language, deep-neural-networks, efficientnetb0, image-classification, python, pytorch, sign-language-recognition, streamlit
- Language: Python
- Homepage:
- Size: 15.3 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# American Sign Language Detection using EfficientNetB0
This project utilizes the **EfficientNetB0** CNN architecture for image classification + detection of American Sign Language (ASL) alphabet images.
## Setup and Installation
### 1. Install Required Dependencies
```bash
pip install -r requirements.txt
pip install kaggle
```
### 2. Download the ASL Alphabet Dataset
To download the dataset directly from Kaggle, ensure that your `kaggle.json` API key is properly set up. Follow these steps:
1. Move your `kaggle.json` file to the directory:
- On Windows: `C:/Users/{your_username}/.kaggle/`
- On macOS/Linux: `~/.kaggle/`
2. Run the following command to download the dataset:
```bash
kaggle datasets download -d debashishsau/aslamerican-sign-language-aplhabet-dataset
```
3. Unzip the dataset:
```bash
unzip aslamerican-sign-language-aplhabet-dataset.zip
```
### 3. Train the Model
You can train the model locally using the dataset:
- Ensure you have at least **8GB of VRAM** on a CUDA-supported GPU.
- Open the `ASL_Alphabet_Classification.ipynb` notebook and follow the instructions for training.
> [!Note]
> Training may take around 1 hour to complete. After training, the model file `efficientnet_model.pth` will be saved under the `models/` directory.
### 4. Running the Demo
#### Streamlit Demo (Image Classification):
```bash
cd demo
streamlit run main.py
```
#### Live Detection Demo:
```bash
python3 detect.py
```
> [!Note]
> If you don't have a webcam, you can use the DroidCam app to turn your mobile phone into a webcam. Logs will be saved in the `action_handler.log` file.
### 5. Pre-trained Model
You can use the pre-trained model `efficientnet_model.pth` located in the `models/` directory to perform inference on ASL images in your local environment or in Google Colab.
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Feel free to report any issues you encounter.
Don't forget to ⭐ the repo!