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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 963 Bytes
7d64bbf a1548ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"experiment": "uchen_ume_binary",
"stage_run": "test",
"test_metrics": {
"loss": 0.48820294297059763,
"accuracy": 0.8073817762399077,
"macro_f1": 0.7078823289680483,
"weighted_f1": 0.8394339697286689,
"auc_roc": 0.9698679503367003
},
"history": {},
"report": " precision recall f1-score support\n\n uchen 0.37 0.98 0.54 99\n ume 1.00 0.79 0.88 768\n\n accuracy 0.81 867\n macro avg 0.68 0.88 0.71 867\nweighted avg 0.93 0.81 0.84 867\n",
"splits_file": "/root/script-classification-model-train/experiments/uchen_ume_binary/checkpoints/uchen_ume_binary/splits.json",
"skip_stage_c": false,
"stage_c_skip_reason": null,
"best_checkpoint": "best_stage_c_last_blocks.pt",
"confusion_matrix": [
[
97,
2
],
[
165,
603
]
]
}
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