Image Classification
Transformers
Safetensors
siglip2_hier_doc
feature-extraction
siglip2
document-classification
hierarchical
multi-task
custom_code
Instructions to use ekacare/med-doc-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ekacare/med-doc-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ekacare/med-doc-classifier", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ekacare/med-doc-classifier", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
eka-doc-classifier: base1024 hier (flat L2 default + scope/l1 select, quality 1-100 score)
29467f3 verified | """Minimal usage example. Run: python example.py path/to/image.jpg""" | |
| import sys | |
| from transformers import AutoModel | |
| from PIL import Image | |
| model = AutoModel.from_pretrained(".", trust_remote_code=True).eval() | |
| img = Image.open(sys.argv[1] if len(sys.argv) > 1 else "doc.jpg") | |
| import json | |
| print(json.dumps(model.classify(img), indent=2)) | |