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: 323 Bytes
29467f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Siglip2ImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_num_patches": 1024,
"patch_size": 16,
"resample": 2,
"rescale_factor": 0.00392156862745098
}
|