Instructions to use BDRC/tibetan-page-orientation-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BDRC/tibetan-page-orientation-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BDRC/tibetan-page-orientation-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("BDRC/tibetan-page-orientation-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,356 Bytes
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"repo_id": "BDRC/dinov3-binary-page-orientation",
"task": "binary_orientation",
"experiment": "dinov3_binary_flip_letterbox",
"classes": [
"non_flipped",
"flipped"
],
"model_id": null,
"preprocess": {
"train": "center_crop",
"val": "center_crop",
"test": "center_crop",
"size": 448
},
"test_metrics": {
"loss": 0.12369998376947376,
"accuracy": 1.0,
"macro_f1": 1.0,
"weighted_f1": 1.0,
"auc_roc": 1.0
},
"training_config": {
"lr_head_a": 0.0005,
"lr_backbone_b": 5e-06,
"lr_head_b": 5e-05,
"lr_backbone_c": 2e-06,
"lr_head_c": 1e-05,
"epochs_a": 5,
"epochs_b": 10,
"epochs_c": 8,
"unfreeze_blocks_b": 4,
"unfreeze_blocks_c": 8,
"skip_stage_c": false,
"scheduler": "cosine_warmup",
"warmup_epochs_a": 0,
"warmup_epochs_b": 2,
"warmup_epochs_c": 2,
"warmup_start_factor": 0.01,
"min_lr_ratio": 0.0,
"weight_decay": 0.01,
"grad_clip_norm": 1.0,
"class_weight_mode": "none",
"class_weights": null,
"label_smoothing": 0.05,
"dropout": 0.1,
"early_stop_patience": 3,
"early_stop_min_delta": 0.001,
"resolved_class_weights": null,
"pooling": "cls_token",
"lambda_consistency": 0.5,
"flip_op": "rot180"
},
"data_dir": null,
"flipped_data_dir": null,
"splits_json": null
}
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