Image Classification
Transformers
ONNX
English
multi-head-classification
room-classification
dinov2
computer-vision
scene-classification
Instructions to use ondame/image-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ondame/image-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ondame/image-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ondame/image-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- `image_classifier_model_0.2_model_info.json`: 모델 메타데이터
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- `image_classifier_model_0.2_inference_example.py`: 추론 예제 코드
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## 성능
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### 전체 성능 (평균)
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- **정확도**: 85.6%
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- **F1-Score**: 84.8%
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- **처리 속도**: 100 이미지/초 이상 (ONNX 모델 기준)
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### 헤드별 성능
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| 헤드 | 정확도 | Precision | Recall | F1-Score |
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|------|--------|-----------|--------|----------|
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| **Scene** | 85.2% | 84.1% | 85.2% | 84.6% |
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| **Concept** | 92.8% | 91.9% | 92.8% | 92.3% |
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| **Object** | 78.9% | 76.4% | 78.9% | 77.6% |
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## 모델 아키텍처
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- `image_classifier_model_0.2_model_info.json`: 모델 메타데이터
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- `image_classifier_model_0.2_inference_example.py`: 추론 예제 코드
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## 모델 아키텍처
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