VisionIQ

Project Description

This project is aimed at developing an object detection system capable of identifying and counting individuals within images or video streams. The underlying models are built upon the Faster R-CNN and YOLO algorithms, leveraging the robustness and efficiency inherent in these architectures. In order to facilitate integration into various real-world applications, an API has been constructed using FastAPI. This allows for a seamless interaction with the object detection system across different environments.

Table of Contents

1. Installation
2. Usage
3. API Documentation
4. Model Architecture
5. Performance Metrics
6. Future Work
7. Contributing
8. License

Installation

Prerequisites

• Python 3.x
• PyTorch
• FastAPI
• Uvicorn

# Clone the repository
git clone https://github.com/dev-tyta/HumanCount.git

# Navigate to the project directory
cd project-repo

# Install the required dependencies
pip install -r requirements.txt

Usage

To start the FastAPI server:

uvicorn main:app --reload

This will start the server on http://127.0.0.1:8000. You can now interact with the API using the endpoints described in the API Documentation section.

API Documentation

The API provides the following endpoints:

• /detect: Accepts an image or video stream and returns the detected objects along with the count of individuals present.
• /metrics: Provides performance metrics of the underlying models.

... (additional endpoints and details)

Model Architecture

Faster R-CNN

Provide a brief overview of the Faster R-CNN architecture, its advantages, and how it has been implemented in this project.

YOLO

Provide a brief overview of the YOLO architecture, its advantages, and how it has been implemented in this project.

Performance Metrics

Discuss the performance metrics used to evaluate the models, and provide the evaluation results.

Future Work

Discuss any planned improvements, additional features or optimizations.

Contributing

Provide information on how others can contribute to the project.

License

Include license information.

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This template provides a structured way to document your project, explaining what the project is about, how to install and use it, detailing the API, the model architectures, performance metrics, and future work. The sections can be expanded or modified to better fit the specifics of your project and to provide more detailed information where necessary.