This repository contains a Jupyter notebook for detecting fruits using the YOLO (You Only Look Once) V8 model. The model is trained and evaluated on a custom dataset of fruit images.
The goal of this project is to build a fruit detection model using the YOLO V8 architecture. The notebook demonstrates the following steps:
- Data Preprocessing
- Model Training
- Model Evaluation
- Visualization of Results
The dataset used in this project consists of images of various fruits. Each image is annotated with bounding boxes indicating the locations of the fruits.
To run the notebook, you need to have Python and Jupyter installed. Additionally, you need to install the required libraries specified in the requirements.txt file.
pip install -r requirements.txt-
Clone the repository:
git clone https://github.com/your_username/fruit-detection-yolo-v8.git cd fruit-detection-yolo-v8 -
Open the Jupyter notebook:
jupyter notebook Fruit\ Detection\ Using\ YOLO\ V8.ipynb
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Run the cells in the notebook to preprocess the data, train the model, and evaluate the results.
The final model is evaluated on the test set, and the notebook includes visualization of detected fruits in sample images.
Contributions are welcome! Please fork the repository and create a pull request with your changes.
This project is licensed under the MIT License - see the LICENSE file for details.