YOLOv8 Ultralytics Classification Test with PyTorch

This repository contains a script for testing image classification using a YOLOv8 model. The script performs classification on a given image, saves the results to text files, and annotates the image with classification labels and confidence scores.

Features

• YOLOv8 Model: Utilizes a pretrained YOLOv8 classification model for image classification tasks.
• Classification Results: Saves classification results to text files with labels and confidence scores.
• Image Annotation: Annotates images with classification labels and confidence scores, and saves them.

Installation

To run this script, you'll need to have the following dependencies installed:

• Python 3
• Ultralytics YOLO (Install via pip install ultralytics)
• OpenCV (Install via pip install opencv-python)
• Pathlib (Included in Python 3.4+)

You can install the required Python packages using:

pip install ultralytics opencv-python

Usage

1. Set Model Path:

   • Update the MODEL_PATH variable with the path to your pretrained YOLOv8 model.

2. Set Image Path:

   • Update the SOURCE variable with the path to the image you want to test.

3. Run the Script:

   • Execute the script to perform image classification.

4. Results:

   • Classification results will be saved in text files in the results directory.
   • Annotated images will also be saved in the same directory.

Repository

• Download the repository: https://github.com/Gulciha-n/yolov8-ultralytics-classification-test

Contributing

We welcome contributions to this project! If you have suggestions, improvements, or bug fixes, please submit an issue or a pull request on GitHub. Your feedback and contributions are greatly appreciated.