readme

README.md

@@ -1,19 +1,148 @@
-# README
+# CatBreed - Cat Breed Recognition Desktop Application
 
-## About
+A deep learning-based cat breed recognition desktop application built with Wails framework, supporting image upload, breed prediction, and history management.
 
-This is the official Wails Preact-TS template.
+![Wails](https://img.shields.io/badge/Wails-Desktop%20App-blue)
+![Preact](https://img.shields.io/badge/Preact-Frontend%20TS-green)
+![Go](https://img.shields.io/badge/Go-Backend-blue)
+![ONNX](https://img.shields.io/badge/ONNX-Inference-orange)
 
-You can configure the project by editing `wails.json`. More information about the project settings can be found
+## Features
-here: https://wails.io/docs/reference/project-config
 
-## Live Development
+- **📸 Image Upload** - Drag & drop or click to upload cat photos (JPG/PNG/JPEG)
+- **🔍 Breed Recognition** - Recognizes 12 common cat breeds based on ResNet18 deep residual network
+- **📊 Confidence Display** - Shows model prediction confidence percentage
+- **📜 History** - Auto-saves each recognition record with pagination support
+- **🗑️ Record Management** - Delete individual records or clear all history
+- **💾 Local Storage** - Uses SQLite database for history data
 
-To run in live development mode, run `wails dev` in the project directory. This will run a Vite development
+## Architecture
-server that will provide very fast hot reload of your frontend changes. If you want to develop in a browser
-and have access to your Go methods, there is also a dev server that runs on http://localhost:34115. Connect
-to this in your browser, and you can call your Go code from devtools.
 
-## Building
+```
+┌─────────────────────────────────────────────────────┐
+│                    Wails Framework                  │
+├──────────────────────┬──────────────────────────────┤
+│   Frontend (Preact) │      Backend (Go)             │
+│   ─────────────────  │   ────────────────────────── │
+│   • Main.tsx         │   • app.go (API)             │
+│   • History.tsx      │   • types.go (Data Models)   │
+│   • Modal.tsx        │   • ONNX Runtime Inference   │
+│   • Component Logic  │   • GORM + SQLite            │
+└──────────────────────┴──────────────────────────────┘
+                              │
+                              ▼
+                    ┌─────────────────┐
+                    │   ONNX Model    │
+                    │   ResNet18      │
+                    │   12 Classes    │
+                    └─────────────────┘
+```
 
-To build a redistributable, production mode package, use `wails build`.
+## Project Structure
+
+```
+dissertation/
+├── backend/                   # Go backend
+│   ├── app.go                 # Core business logic
+│   └── types.go               # Data structure definitions
+├── core/                      # Python ML module
+│   ├── nets/                  # Neural network definitions
+│   │   ├── resnet.py          # ResNet base class
+│   │   └── resnet18.py        # ResNet18 model
+│   ├── dataloader/            # Data loading
+│   ├── train/                 # Training scripts
+│   │   ├── train_resnet18.py  # Model training
+│   │   └── to_onnx.py         # PyTorch → ONNX conversion
+│   └── const/                 # Hyperparameter configuration
+├── frontend/                  # Preact + TypeScript frontend
+│   └── src/
+│       └── components/        # React-style components
+│           ├── Main.tsx       # Main page (upload/recognition)
+│           └── History.tsx    # History page
+├── static/images/             # Uploaded image storage directory
+└── app.db                     # SQLite database
+```
+
+## Quick Start
+
+### Requirements
+
+- Go 1.21+
+- Node.js 18+
+- Python 3.9+ (for model training)
+- Wails CLI
+
+### Install Dependencies
+
+```bash
+# Install Wails CLI
+go install github.com/wailsapp/wails/v2/cmd/wails@latest
+
+# Install frontend dependencies
+cd frontend
+npm install
+
+# Return to project root
+cd ..
+```
+
+### Run the Application
+
+```bash
+# Development mode
+wails dev
+
+# Production build
+wails build
+```
+
+### Model Training (Optional)
+
+To retrain the model:
+
+```bash
+cd core/train
+
+# Train ResNet18
+python train_resnet18.py
+
+# Export to ONNX format
+python to_onnx.py
+```
+
+The trained model file `resnet18_epoch_50.onnx` should be placed in the `build/bin` directory of the output.
+
+## Technical Details
+
+### Inference Pipeline
+
+1. **Image Preprocessing** - Resize uploaded image to 224×224, apply ImageNet normalization
+   - Mean: `[0.485, 0.456, 0.406]`
+   - Std: `[0.229, 0.224, 0.225]`
+2. **Tensor Format** - Convert to NCHW format `(1, 3, 224, 224)`
+3. **ONNX Inference** - Execute inference via ONNX Runtime Go
+4. **Post-processing** - Softmax for confidence calculation, return highest probability class
+
+### Data Models
+
+| Table | Description |
+|-------|-------------|
+| `breeds` | Cat breed table (code, name, brief description) |
+| `history` | Recognition history (image, breed, confidence, timestamp) |
+
+## UI Preview
+
+### Main Page
+- Upload area supports drag & drop
+- Real-time image preview
+- One-click breed recognition
+- Result display with confidence progress bar
+
+### History Page
+- Paginated history records
+- Click image to view details
+- Single/batch delete functionality
+
+## License
+
+MIT