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---
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. **κ°κ΅¬** (Furniture) - beds, sofas, desks, chairs
2. **μνμ©ν** (Household items) - kitchenware, cleaning supplies, storage
3. **μ μκΈ°κΈ°_λμ** (Electronics/Books) - smartphones, laptops, books, e-books
4. **μ·¨λ―Έ_κ²μ** (Hobbies/Games) - game consoles, board games, sports equipment
5. **ν¨μ
_λ·°ν°** (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/
βββ ensemble/ # Ensemble models (349MB)
β βββ EfficientNetB0_best.keras
β βββ ResNet50V2_best.keras
β βββ ... (7 models)
βββ serving/ # Serving models
βββ model_optimized.tflite # 24MB
βββ TensorFlowLiteInferenceService.java
```
## License
MIT License
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