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Training/training.ipynb: Jupyter notebook containing all the training code for the YOLOv5 model. This includes data preprocessing, model configuration, training, and evaluation steps.
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Training/Requirements for virtual environment for training.txt: A text file listing all the required Python packages and dependencies needed to create a virtual environment for training the YOLOv5 model.
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Web/Requirements for virtual environment for web interface.txt: A text file listing all the required Python packages and dependencies to set up the Flask-based web interface.
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Web/Backend/app.py: Python script that runs the Flask backend for the web application. It handles image uploads, runs the YOLOv5 model for inference, and returns the detected results to the frontend.
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Web/Frontend/Static/index.css CSS file that styles: the frontend of the web application, providing layout, formatting, and visual enhancements for the image upload page.
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Web/Frontend/Static/index.js: JavaScript file that adds interactive functionality to the frontend, such as handling the image upload process and displaying results.
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Web/Frontend/Template/index.html: HTML file that structures the frontend of the web application. It contains the layout for the image upload form and the display of object detection results.
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This project trains a YOLOv5 model for object detection and deploys it through a web interface. The web application is built using Flask for the backend and HTML/CSS/JavaScript for the frontend. Users can upload images to the website, and the model will detect objects and display the results on the same page.
abhinavrajgupta/CleanScan-AI-Powered-Waste-Detection
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This project trains a YOLOv5 model for object detection and deploys it through a web interface. The web application is built using Flask for the backend and HTML/CSS/JavaScript for the frontend. Users can upload images to the website, and the model will detect objects and display the results on the same page.
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