This project implements a trash detection system using YOLOv5, a state-of-the-art object detection model. It can identify various types of trash in images or video streams, which can be useful for automated waste sorting, litter monitoring, and other environmental applications.

The system uses a custom-trained YOLOv5 model to detect and classify different categories of trash. It provides real-time detection capabilities and can be adapted to various use cases.

• Object Detection: Detects and classifies different types of trash (e.g., plastic, paper, metal, cardboard, etc.). You'll need to define the specific classes relevant to your project.
• Real-time Processing: Can process video streams or live camera feeds for real-time trash detection.
• Image Processing: Can also analyze individual images for trash detection.
• Customizable: The YOLOv5 model can be trained on a custom dataset to improve accuracy for specific types of trash or environments.
• Performance Metrics: Provides tools for evaluating the model's performance (e.g., precision, recall, mAP).

• Python: The primary programming language.
• Ultralytics YOLOv5: The object detection framework.
• OpenCV (cv2): For image and video processing.
• Google Colab: For running python code.