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README.md

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+# LiteCNNPro Model - Pure C++ Inference
+
+**Ultra-lightweight CNN model for dog breed classification**
+
+## Model Details
+
+- **Model**: LiteCNNPro (Pure C++ implementation)
+- **Parameters**: 600K
+- **Classes**: 120 (Stanford Dogs dataset)
+- **Input**: 224×224 RGB images
+- **Framework**: PyTorch (training) → Pure C++ (inference)
+- **Memory**: 26MB total (4MB weights + 22MB runtime)
+
+## Architecture
+
+```
+Stem: Conv2D(3→32) + BatchNorm + ReLU6
+Features: 7× Depthwise Separable Conv blocks
+  - Block 0: 32→64 (stride 2)
+  - Block 1: 64→128 (stride 2)
+  - Block 2-3: 128→256 (stride 2)
+  - Block 4-6: 256→512
+  - SE (Squeeze-Excitation) attention in each block
+Classifier: AdaptiveAvgPool → FC(512→256) → FC(256→120)
+```
+
+## Usage
+
+### Download Model
+
+```bash
+wget https://huggingface.co/2c6829/litecnn-pure-cpp/resolve/main/model_weights.bin
+wget https://huggingface.co/2c6829/litecnn-pure-cpp/resolve/main/breed_classes.json
+```
+
+### Build and Run
+
+```bash
+# Clone the inference server
+git clone https://github.com/stupidcoderJung/litecnn-pure-cpp
+cd litecnn-pure-cpp
+
+# Place model files
+mv model_weights.bin weights/
+mv breed_classes.json .
+
+# Build
+mkdir -p build && cd build
+cmake .. -DCMAKE_BUILD_TYPE=Release
+make -j4
+
+# Run server
+./litecnn_server --port 8080
+```
+
+### API Example
+
+```bash
+# Health check
+curl http://localhost:8080/health
+
+# Predict
+curl -X POST http://localhost:8080/predict \
+  -F "image=@dog.jpg"
+```
+
+**Response**:
+```json
+{
+  "predictions": [
+    {
+      "class_id": 81,
+      "score": 0.95,
+      "breed_en": "Border collie",
+      "breed_ko": "보더 콜리"
+    }
+  ]
+}
+```
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Memory (RSS) | 26 MB |
+| Binary Size | 803 KB |
+| Weights Size | 4.0 MB |
+| Inference Time | <100ms (CPU) |
+
+**Comparison**:
+- PyTorch: 322 MB → **92% reduction** ✅
+- LibTorch: 130 MB → **80% reduction** ✅
+- ONNX Runtime: 102 MB → **75% reduction** ✅
+
+## Files
+
+- `model_weights.bin` (4.0 MB) - Model weights in binary format
+- `breed_classes.json` (7.4 KB) - 120 dog breeds (English + Korean)
+- `extract_weights.py` - PyTorch checkpoint → binary converter
+
+## Training
+
+The model was trained on the Stanford Dogs dataset with:
+- Optimizer: AdamW
+- Learning rate: 1e-3
+- Augmentation: Random flip, rotation, color jitter
+- Epochs: 50
+- Best validation accuracy: ~85%
+
+## License
+
+MIT License
+
+## Citation
+
+```bibtex
+@software{litecnn_pure_cpp_2026,
+  author = {LiteCNN Team},
+  title = {LiteCNN Pure C++ Inference Server},
+  year = {2026},
+  url = {https://github.com/stupidcoderJung/litecnn-pure-cpp}
+}
+```
+
+## Contact
+
+- Repository: https://github.com/stupidcoderJung/litecnn-pure-cpp
+- Issues: https://github.com/stupidcoderJung/litecnn-pure-cpp/issues