Object Detection with YOLO

This project implements Object Detection using the YOLO (You Only Look Once) algorithm, which is a fast and accurate deep learning model for object detection. YOLO divides the image into a grid, then predicts bounding boxes and probability scores for each region in the image.

🚀 Project Overview

In this project, we aim to detect objects from images or videos in real-time using the YOLO model. This repository provides an easy-to-use pipeline for training YOLO on your custom dataset or using pretrained weights for object detection.

This project uses YOLOv5/YOLOv8 (or any YOLO version your project is based on) for performing multi-class object detection tasks like detecting vehicles, pedestrians, animals, etc.

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📝 Table of Contents

• Features
• Setup and Installation
• Usage Instructions
• Training YOLO on Custom Dataset
• Evaluating the Model
• Results
• Project Structure
• References

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✨ Features

• Integration of YOLO for real-time object detection
• Easy setup for inference with pre-trained or custom models
• Supports both image and video input
• Custom dataset support for training (using YOLO configuration)
• Visualization of detection results (bounding boxes, labels, confidence scores)
• Uses PyTorch for GPU-based computation

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⚙️ Setup and Installation

Follow these steps to set up the project on your local machine:

1. Clone the repository:

   git clone https://github.com/your-username/object_detection_with_yolo.git
   cd object_detection_with_yolo

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

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

3. Install dependencies:

   pip install -r requirements.txt

   The dependencies typically include:

   • torch: Deep learning framework for YOLO model
   • opencv-python: For image processing
   • PyYAML: For configuration files
   • matplotlib: For visualizing detection results
   • tqdm: For progress bars

4. Download pre-trained YOLO weights

   • For YOLOv8 (depending on your YOLO version):

     # Command to download weights for YOLOv8 model goes here

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🧑‍💻 Usage Instructions

Object Detection on Images:

1. Run YOLO detection on an image:

   python detect.py --source path_to_image.jpg --weights path_to_yolov_weights.pt --output output_folder

2. Parameters:

   • --source: Path to the input image or folder containing images
   • --weights: Path to YOLO model weights
   • --output: Folder to store the output results
   • Other options include --img-size, --conf-threshold, --iou-threshold, etc.

Object Detection on Video:

1. Run YOLO detection on a video:

   python detect.py --source path_to_video.mp4 --weights path_to_yolov_weights.pt --output output_folder

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🧮 Evaluating the Model

Once the model is trained, you can evaluate the performance using the validation set:

python val.py --weights path_to_weights.pt --data custom_data.yaml --task test

• This will output metrics like [precision, recall, mAP (mean Average Precision)] for object detection performance.

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📚 References

• YOLOv8 Official Repository
• YOLO Tutorial and Guide
• PyTorch Documentation
• COCO Dataset

Make sure to check these resources for better understanding and guidance.

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