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 | { | |
| "flat_l2": { | |
| "bf16": { | |
| "internal": { | |
| "acc": 96.45, | |
| "macro_f1": 0.9646, | |
| "cluster_acc": 97.36, | |
| "n": 16204 | |
| }, | |
| "external": { | |
| "acc": 80.9, | |
| "macro_f1": 0.7019, | |
| "cluster_acc": 90.95, | |
| "n": 953 | |
| }, | |
| "img_per_s": 78.4 | |
| }, | |
| "int8": { | |
| "internal": { | |
| "acc": 96.43, | |
| "macro_f1": 0.9645, | |
| "cluster_acc": 97.36, | |
| "n": 16204 | |
| }, | |
| "external": { | |
| "acc": 80.9, | |
| "macro_f1": 0.6527, | |
| "cluster_acc": 91.15, | |
| "n": 953 | |
| }, | |
| "img_per_s": 69.3 | |
| }, | |
| "int4": { | |
| "internal": { | |
| "acc": 95.22, | |
| "macro_f1": 0.9553, | |
| "cluster_acc": 94.23, | |
| "n": 16204 | |
| }, | |
| "external": { | |
| "acc": 77.23, | |
| "macro_f1": 0.6304, | |
| "cluster_acc": 85.6, | |
| "n": 953 | |
| }, | |
| "img_per_s": 24.8 | |
| } | |
| }, | |
| "internal_n": 16204, | |
| "external_n": 953 | |
| } |