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.
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.
- 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.
- Clone this repository:
git clone https://github.com/your-username/YOLOV5CustomDataset.git cd your-repo-name - Install the requirements
pip install -r requirements.txt
- 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 │ │ │ └── ...
- 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
- 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
- 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 - Results The training and evaluation results, including loss curves and other metrics, can be found in the runs/ directory.
The original YOLOv5 repository: Ultralytics YOLOv5