Crafted With Love by Sam Naveenkumar .V

Welcome to Prodigy-ML-04, a comprehensive hand gesture recognition model designed to accurately identify and classify different hand gestures from image or video data. This project aims to enable intuitive human-computer interaction and gesture-based control systems, making technology more accessible and interactive.

• Accurate Gesture Recognition: Leverage a convolutional neural network (CNN) to classify hand gestures with high accuracy.
• Real-time Predictions: Integrate with webcam or video feed to provide real-time gesture recognition.
• Easy to Use: Simple and clean code structure for easy understanding and modification.
• Extensible: Easily extend the model to include more gestures or improve accuracy with additional data.

This project uses the Leap Gesture Database from Kaggle, which can be found here.

• A Google account to use Google Colab.
• Basic understanding of Python and machine learning concepts.

1. Open the Colab Notebook:

2. Setup Kaggle API Key:

   To download the dataset, you'll need a Kaggle API key. Follow these steps:

   • Sign in to your Kaggle account.
   • Go to Account and select Create New API Token to download the kaggle.json file.
   • Upload the kaggle.json file in the Colab notebook.

3. Run the Notebook:

   Execute each cell in the notebook sequentially. The notebook includes:

   • Downloading the Dataset: Automatically download and unzip the dataset from Kaggle.
   • Training the Model: Train the hand gesture recognition model using the provided dataset.
   • Real-time Predictions: Use the webcam to make real-time gesture predictions or upload an image/video for prediction.

4. Saving and Loading the Model:

   The trained model will be saved to your Google Drive. You can load the saved model for future predictions without retraining.

The model uses a convolutional neural network (CNN) with the following architecture:

• Conv2D: 32 filters, 3x3 kernel, ReLU activation
• MaxPooling2D: 2x2 pool size
• Conv2D: 64 filters, 3x3 kernel, ReLU activation
• MaxPooling2D: 2x2 pool size
• Conv2D: 128 filters, 3x3 kernel, ReLU activation
• MaxPooling2D: 2x2 pool size
• Flatten
• Dense: 128 units, ReLU activation
• Dropout: 50%
• Dense: 8 units, Softmax activation (for 8 gesture classes)

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue to improve this project.

This project is licensed under the MIT License. See the LICENSE file for details.

• The dataset used in this project is provided by Leap Gesture Database on Kaggle.
• Special thanks to the contributors and the open-source community for their valuable resources and support.