rezaul-h/CottonVerse: CottonVerse

Version 1.0.0 — Initial release CottonVerse 🌿 CottonVerse is a multi-model Flask-based web application for image classification tasks in the agricultural and textile domains. It enables interpretable prediction using Grad-CAM and supports four independent models: 🍃 Cotton Leaf Disease (CottonLeafNet) 🌿 Cotton Leaf Disease (SAR-CLD-2024) 🧵 Fabric Texture Percentage (CottonFabricImageBD) 🩸 Fabric Stain Type (FabricSpotDefect) 🚀 Features 📁 Drag-and-drop file upload ✅ Prediction probabilities with visual bar chart 🔥 Grad-CAM visualization for interpretability 📸 Upload preview and CAM download 🖼️ Multiple datasets and models supported 🧠 Powered by LEViT model (via PyTorch + timm) 🎨 Responsive, modern, and interactive UI 📂 Project Structure cottonverse/ ├── app.py # Flask app ├── utils.py # Preprocessing & Grad-CAM logic ├── models/ # Trained .pth models ├── static/ │ ├── uploads/ # Uploaded images │ └── cams/ # Grad-CAM outputs ├── templates/ # HTML templates └── requirements.txt # Dependencies └── README.md ⚙️ Installation Clone the repository: git clone https://github.com/rezaul-h/CottonVerse.git cd cottonverse Create and activate a virtual environment: python -m venv venv source venv/bin/activate # or venv\Scripts\activate Install dependencies: pip install -r requirements.txt Run the application: python app.py Open http://127.0.0.1:5000 in your browser 🌐 📊 Models & Datasets | Model Name | Dataset | Classes | |------------|------------------------|---------| | LEViT | CottonLeafNet | 8 | | LEViT | SAR-CLD-2024 | 9 | | LEViT | CottonFabricImageB | 13 | | LEViT | FabricSpotDefect | 12 | All models are trained using the LEViT architecture with PyTorch, saved in .pth format. 🧪 How to Use Click on a model on the landing page. Upload an image for classification. View prediction probabilities and Grad-CAM. Download the CAM if desired. 📦 Dependencies Flask PyTorch timm torchvision OpenCV matplotlib pytorch-grad-cam 🙏 Acknowledgements LEViT model: Facebook AI GradCAM: Jacob Gildenblat (pytorch-grad-cam)