Welcome to the WeedBuster project! This repository contains the code and pre-trained weights for detecting weeds using the YOLOv8 model. The model has been trained on both CPU and GPU environments.

WeedBuster is a machine learning project designed to detect weeds in images using the YOLOv8 object detection model. The goal is to assist farmers and agricultural professionals in identifying and managing weed infestations efficiently.

• Trained on both CPU and GPU for versatility
• High accuracy in weed detection
• Easy-to-use interface for uploading and analyzing images

Report: [https://api.wandb.ai/links/fivibi5910/h5jxlo1]

Dataset: [https://www.kaggle.com/datasets/swish9/weeds-detection]

To get started with WeedBuster, clone the repository and install the necessary dependencies:

git clone https://github.com/yourusername/WeedBuster-YOLOv8-Weeds-Detection.git
cd WeedBuster-YOLOv8-Weeds-Detection
pip install -r requirements.txt