Clarify code and weights licenses

de41fdd verified 9 days ago

4.22 kB

license: other
license_name: dinov3-license
license_link: >-
  https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m/blob/main/LICENSE.md
language:
  - bo
tags:
  - image-classification
  - tibetan
  - script-classification
  - dinov3
  - binary
library_name: transformers
pipeline_tag: image-classification
base_model: facebook/dinov3-vits16-pretrain-lvd1689m
datasets:
  - BDRC/danyig-pedri-binary-balanced-script-classification-dataset
metrics:
  - f1
  - accuracy
  - auc

Danyig vs Pedri Binary Script Classifier (DINOv3 ViT-S)

Fine-tuned DINOv3 ViT-S for parent script classification:

Danyig, Pedri

Experiment: dinov3_danyig_pedri_binary (danyig_pedri_binary_classification)
Pooling: ViT CLS token (last_hidden_state[:, 0, :])
Weights: final_model.pt (best validation macro-F1 across stages A/B/C)

Data

Split	Source
Train / val / test	BDRC/danyig-pedri-binary-balanced-script-classification-dataset

Test split: balanced benchmark (60 images per parent class, held out of training).

Preprocessing

Split	Mode	Size
train	`resize_letterbox`	448
val	`resize_letterbox`	448
test	`resize_letterbox`	448

Validation metrics (n=118)

Metric	Value
Accuracy	81.4%
Macro F1	0.814
Weighted F1	0.813
AUC-ROC	0.863
Loss	0.5729

Best checkpoint: best_stage_c_last_blocks.pt epoch 12 val macro-F1 0.814

Per-class (validation)

precision    recall  f1-score   support

      Danyig       0.84      0.78      0.81        60
       Pedri       0.79      0.84      0.82        58

    accuracy                           0.81       118
   macro avg       0.81      0.81      0.81       118
weighted avg       0.82      0.81      0.81       118

Test / benchmark metrics (n=120)

Metric	Value
Accuracy	85.0%
Macro F1	0.849
Weighted F1	0.849
AUC-ROC	0.914
Loss	0.4417

Per-class (test)

precision    recall  f1-score   support

      Danyig       0.90      0.78      0.84        60
       Pedri       0.81      0.92      0.86        60

    accuracy                           0.85       120
   macro avg       0.86      0.85      0.85       120
weighted avg       0.86      0.85      0.85       120

Training

Stage	Epochs	LR head	LR backbone	Unfrozen blocks
A	7	0.0005	—	0
B	10	0.0001	1e-05	4
C	12	5e-05	1.5e-05	8

Setting	Value
Scheduler	`cosine_warmup`
Class weights	`custom`
Label smoothing	0.05
Dropout	0.1

Confusion matrix (test)

True \ Pred	Danyig	Pedri
Danyig	47	13
Pedri	5	55

Files

File	Description
`final_model.pt`	Best val-F1 weights + label maps
`results.json`	Full metrics, history, warm-start info
`config.yaml`	Training config
`model_card.json`	Summary metadata
`confusion_matrix.json` / `.png`	Test CM
`training_history.png`	Stage loss / val F1 curves
`split_stats.json` / `.md`	Per-class split counts
`inference.py`	Classify image paths
`requirements-inference.txt`	Pip deps

Inference

pip install -r requirements-inference.txt
python inference.py --checkpoint final_model.pt --image path/to/page.jpg --preprocess resize_letterbox --preprocess-size 448

Reproduce training

python experiments/danyig-pedri-subclass/train.py

Model repo: BDRC/danyig-pedri-binary-script-classifier

License

The fine-tuned model weights are derivative works of DINOv3 and are distributed under the DINOv3 License. The original inference code in this repository is available under the Apache License 2.0.