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
| { | |
| "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 | |
| ] | |
| ] | |
| } | |