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
File size: 337 Bytes
29467f3 | 1 2 3 4 5 6 7 8 9 10 11 | """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))
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