Instructions to use BDRC/gyuyig-tsugdri-binary-script-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BDRC/gyuyig-tsugdri-binary-script-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BDRC/gyuyig-tsugdri-binary-script-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/gyuyig-tsugdri-binary-script-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| experiment: dinov3_gyuyig_tsugdri_binary | |
| task: gyuyig_tsugdri_binary_classification | |
| balanced_dataset_repo: BDRC/gyuyig-tsugdri-binary-balanced-script-classification-dataset | |
| val_ratio: 0.15 | |
| warmstart_repo: BDRC/4-class-balanced-script-classifier | |
| warmstart_checkpoint_file: final_model.pt | |
| output_dir: checkpoints | |
| model_id: facebook/dinov3-vits16-pretrain-lvd1689m | |
| seed: 42 | |
| batch_size: 16 | |
| num_workers: 8 | |
| no_amp: false | |
| no_weighted_sampler: true | |
| skip_stage_c: false | |
| gradient_checkpointing: true | |
| train_preprocess: center_crop | |
| val_preprocess: center_crop | |
| test_preprocess: center_crop | |
| preprocess_size: 224 | |
| pooling: cls_token | |
| epochs_a: 7 | |
| epochs_b: 10 | |
| epochs_c: 12 | |
| unfreeze_blocks_b: 4 | |
| unfreeze_blocks_c: 8 | |
| lr_head_a: 5.0e-4 | |
| lr_backbone_b: 1.0e-5 | |
| lr_head_b: 1.0e-4 | |
| lr_backbone_c: 1.5e-5 | |
| lr_head_c: 5.0e-5 | |
| scheduler: cosine_warmup | |
| warmup_epochs_a: 0 | |
| warmup_epochs_b: 1 | |
| warmup_epochs_c: 1 | |
| warmup_start_factor: 0.01 | |
| min_lr_ratio: 0.01 | |
| weight_decay: 0.02 | |
| grad_clip_norm: 1.0 | |
| dropout: 0.1 | |
| label_smoothing: 0.05 | |
| class_weight_mode: sqrt_inverse_freq | |
| early_stop_patience: 6 | |
| early_stop_min_delta: 0.001 | |