---
license: mit
language:
- ko
tags:
- image-classification
- computer-vision
- korean
- secondhand-market
- e-commerce
- ensemble
- tensorflow
- keras
datasets:
- custom
metrics:
- accuracy
model_type: ensemble
pipeline_tag: image-classification
widget:
- src: https://example.com/sample_image.jpg
  example_title: "Sample Product Image"
base_model:
- google/efficientnet-b0
- microsoft/resnet-50
- facebook/deit-base-distilled-patch16-224
library_name: tensorflow
---
# Korean Secondhand Market Classifier

## Model Overview

AI model for automatic categorization of Korean secondhand market product images.
**70.61% accuracy** achieved with 7-model ensemble system.

## Supported Categories

1. **가구** (Furniture) - beds, sofas, desks, chairs
2. **생활용품** (Household items) - kitchenware, cleaning supplies, storage
3. **전자기기_도서** (Electronics/Books) - smartphones, laptops, books, e-books
4. **취미_게임** (Hobbies/Games) - game consoles, board games, sports equipment
5. **패션_뷰티** (Fashion/Beauty) - clothing, shoes, cosmetics, accessories

## Performance

- **Ensemble Accuracy**: 70.61%
- **Individual Models**: 7 models (EfficientNet, ResNet50V2, DenseNet, etc.)
- **Input Size**: 224x224 RGB images

## Usage

```python
# Install dependencies
pip install fastapi uvicorn tensorflow pillow huggingface_hub

# Download and run
from huggingface_hub import snapshot_download
repo_path = snapshot_download("bihan3876/my_model")

# Run API server
import subprocess
subprocess.run(["python", f"{repo_path}/api_server.py"])
```

## File Structure

```
models/
├── ensemble/              # Ensemble models (349MB)
│   ├── EfficientNetB0_best.keras
│   ├── ResNet50V2_best.keras
│   └── ... (7 models)
└── serving/               # Serving models
    ├── model_optimized.tflite  # 24MB
    └── TensorFlowLiteInferenceService.java
```

## License

MIT License