Image Classification
Transformers
Tibetan
tibetan
uchen
ume
script-classification
dinov3
fine-tuned
Eval Results (legacy)
Instructions to use openpecha/uchen-ume-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openpecha/uchen-ume-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="openpecha/uchen-ume-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openpecha/uchen-ume-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload final_model weights and essential eval artifacts for both variants
Browse files
README.md
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## Repo layout
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```
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without_preprocess/
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final_model.pt
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benchmark_eval_results.json
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with_preprocess/
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final_model.pt
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results.json
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benchmark_eval_results.json
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```
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## Repo layout
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```
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without_preprocess/ ← recommended (full-page test & benchmark)
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final_model.pt ← production weights
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model_card.json
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results.json ← test metrics + classification report
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benchmark_eval_results.json
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with_preprocess/ ← center-crop train/val only; not for full-page deploy
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final_model.pt
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model_card.json
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results.json
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benchmark_eval_results.json
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```
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with_preprocess/benchmark_confusion_matrix.png
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with_preprocess/confusion_matrix.png
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without_preprocess/benchmark_confusion_matrix.png
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without_preprocess/confusion_matrix.png
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