Hand Detection using SSD MobileNet in TensorFlow

This project is focused on developing a deep learning model to detect hands using the SSD MobileNet architecture in TensorFlow. The goal is to create an accurate and efficient hand detection system that can be used in various applications, such as gesture recognition, human-computer interaction, and robotics.

TODO

1. Update requirements.txt file

Table of Contents

1. Introduction
2. Requirements
3. Installation
4. Dataset
5. Training
6. Inference
7. Evaluation
8. Results
9. Contributing
10. License

Introduction

Hand detection is a crucial task in computer vision, and the Single Shot Multibox Detector (SSD) MobileNet model offers an efficient and accurate solution. The SSD architecture is a real-time object detection method that combines the strengths of both object localization and object detection into a single network. MobileNet is a lightweight deep learning model that provides fast inference times, making it suitable for real-time applications on resource-constrained devices.

This project aims to demonstrate how to train the SSD MobileNet model on a custom hand detection dataset and deploy the trained model for practical use.

Requirements

To run this project, you need the following dependencies:

• Python 3.x
• TensorFlow 2.x
• NumPy
• OpenCV

Ensure you have these libraries installed before proceeding with the installation.

Installation

1. Clone the repository:

git clone https://github.com/PG-9-9/Gesture-Net.git
cd Gesture-Net

2. Set up a virtual environment (optional but recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install the required packages:

pip install -r requirements.txt

Dataset

Gather or create a dataset of images labeled with hand annotations. The dataset should be in PASCAL VOC or COCO format. Organize the dataset into train and test sets for training and evaluation purposes. Ensure you have a proper balance of positive and negative samples to achieve good training results.

Training

1. Prepare the dataset: Place the training and testing datasets in the appropriate directories, following the required format.

2. Configure the training settings: Adjust the hyperparameters, batch size, learning rate, and other parameters in the config.py file.

3. Start training: Run the training script using the following command:

python train.py

4. Monitor training progress: During training, you can visualize the loss and other metrics using TensorBoard.

Inference

Once the model is trained, you can use it to detect hands in new images or video streams. Use the following command to perform inference on a single image:

python inference.py --image path/to/your/image.jpg

To perform inference on a video stream, use:

python inference.py --video path/to/your/video.mp4

Evaluation

Evaluate the trained model on the test dataset to measure its performance. Use the following command to assess the model's accuracy:

python evaluate.py

Results

Include a brief summary of the model's performance and any visualizations of the detection results. Provide insights into potential areas for improvement or ways to optimize the model.

Contributing

Contributions to this project are welcome! If you find any issues or have suggestions for improvements, feel free to create a pull request or open an issue.

License

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