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: 3 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 (11 months ago)
• Default Branch: main
• Last Pushed: 2024-09-24T17:51:51.000Z (4 months ago)
• Last Synced: 2024-11-12T06:09:26.693Z (3 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.

---

Feel free to report any issues you encounter.  

Don't forget to ⭐ the repo!