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
Link the medai.eka.care live demo and launch blog
Browse files
README.md
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The model was built by training many specialised models, ensembling them, and distilling the ensemble into this one compact student.
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## Results
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Full internal test = 16,204 images (see `benchmark_results.json`).
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The model was built by training many specialised models, ensembling them, and distilling the ensemble into this one compact student.
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[**Live demo**](https://medai.eka.care/classifier) · [**Launch blog**](https://info.eka.care/services/releasing-parrotlet-open-models-open-weights-for-medical-document-intelligence)
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## Results
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Full internal test = 16,204 images (see `benchmark_results.json`).
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