CatBreed - Cat Breed Recognition Desktop Application

A deep learning-based cat breed recognition desktop application built with Wails framework, supporting image upload, breed prediction, and history management.

Features

📸 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

Architecture

┌─────────────────────────────────────────────────────┐
│                    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    │
                    └─────────────────┘

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

# 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

# Development mode
wails dev

# Production build
wails build

Model Training (Optional)

To retrain the model:

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

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]
Tensor Format - Convert to NCHW format (1, 3, 224, 224)
ONNX Inference - Execute inference via ONNX Runtime Go
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

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