This repository focuses on utilizing YOLOv8 from the Ultralytics library to train an object detection model with a specific dataset.
- Dataset: Car detection folder
- Images: 371 images
- Split: 345 for training, 26 for testing
- Folder Structure:
- val: Contains images and labels folders (26 files each)
- train: Contains images and labels folders (345 files each)
- data.yml: File containing dataset information for model loading Additionally, the repository includes uploaded training model weights and a cloned model from Ultralytics.
The model makes predictions, and the visualize_bbox function displays the results along with bounding boxes of the detected objects.
This project encompasses the following processes:
- Load dataset
- Model training
- Model evaluation
- Prediction
Feel free to explore the repository to understand the implementation and results of YOLOv8 for object detection with the provided dataset.