YOLOv5 Custom Dataset Application

This repository contains my work on using the YOLOv5 object detection model on a custom dataset. The original YOLOv5 repository by Ultralytics can be found here.

Overview

YOLOv5 is a state-of-the-art object detection model that provides excellent accuracy and performance. This project demonstrates how to train YOLOv5 on a custom dataset and evaluate its performance.

Features

• Clone of the original YOLOv5 repository.
• Training of YOLOv5 on a custom dataset.
• Evaluation of the trained model on a custom test set.
• Instructions and scripts for reproducing the results.

Installation

1. Clone this repository:

   git clone https://github.com/your-username/YOLOV5CustomDataset.git
   cd your-repo-name
   

2. Install the requirements

   pip install -r requirements.txt
   

3. Preparing the Custom Dataset

   data/
   ├── my_custom_dataset/
   │   ├── images/
   │   │   ├── train/
   │   │   │   ├── img1.jpg
   │   │   │   ├── img2.jpg
   │   │   │   └── ...
   │   │   ├── val/
   │   │   │   ├── img1.jpg
   │   │   │   ├── img2.jpg
   │   │   │   └── ...
   │   │   ├── test/
   │   │   │   ├── img1.jpg
   │   │   │   ├── img2.jpg
   │   │   │   └── ...
   │   ├── labels/
   │   │   ├── train/
   │   │   │   ├── img1.txt
   │   │   │   ├── img2.txt
   │   │   │   └── ...
   │   │   ├── val/
   │   │   │   ├── img1.txt
   │   │   │   ├── img2.txt
   │   │   │   └── ...
   │   │   ├── test/
   │   │   │   ├── img1.txt
   │   │   │   ├── img2.txt
   │   │   │   └── ...
   

4. Create a dataset configuration file my_custom_dataset.yaml

   train: data/my_custom_dataset/images/train
   val: data/my_custom_dataset/images/val
   test: data/my_custom_dataset/images/test
   
   nc: 2  # number of classes
   names: ['class1', 'class2']  # list of class names
   

5. Training Model To train YOLOv5 on your custom dataset, run:

   python train.py --img 640 --batch 16 --epochs 100 --data data/my_custom_dataset.yaml --weights yolov5s.pt

6. Testing Model To evaluate the trained model on the test set, run:

   python val.py --data data/my_custom_dataset.yaml --weights runs/train/exp/weights/best.pt --img 640 --task test
   

7. Results The training and evaluation results, including loss curves and other metrics, can be found in the runs/ directory.

Acknowledgements

The original YOLOv5 repository: Ultralytics YOLOv5