Introduction
This repository provides a comprehensive guide on using neural networks, specifically the SSD model, to detect hands in various environments using Tensorflow's Object Detection API. The project focuses on the challenges of hand detection, particularly in egocentric views, and offers practical solutions through the use of the Egohands dataset. Key features include:
- Dataset Preparation: Instructions on how to prepare and convert the Egohands dataset into a format suitable for Tensorflow training.
- Model Training: Step-by-step guidance on training a hand detection model using transfer learning, significantly reducing training time.
- Real-time Detection: Implementation of detection algorithms that can be applied to video streams, showcasing the model's performance in real-time scenarios.
- Optimization Techniques: Tips on improving detection speed and accuracy through threading, image processing, and model quantization.
This repository is ideal for developers and researchers interested in computer vision, particularly in applications involving hand tracking and gesture recognition.
