---
language:
- bo
tags:
- image-classification
- tibetan
- page-orientation
- dinov3
library_name: transformers
pipeline_tag: image-classification
base_model: facebook/dinov3-vits16-pretrain-lvd1689m
datasets:
- BDRC/tibetan-page-orientation-classifier-dataset
metrics:
- f1
- accuracy
- auc
license: apache-2.0
---

# Tibetan page orientation classifier (DINOv3 ViT-S)

Predict whether a tibetan manuscript page image is **upright** (`non_flipped`) or **upside-down** (`flipped`, 180°).

Fine-tuned [DINOv3 ViT-S](https://huggingface.co/facebook/dinov3-vits16-pretrain-lvd1689m).

**Dataset (HF):** [BDRC/tibetan-page-orientation-classifier-dataset](https://huggingface.co/datasets/BDRC/tibetan-page-orientation-classifier-dataset)  
**Experiment:** `dinov3_binary_flip_center_cropped`  
**Checkpoint:** `final_model.pt` (best validation macro-F1 across stages A + B + C)

## Preprocessing (inference)

| Split | Mode |
|-------|------|
| train | `center_crop` |
| val | `center_crop` |
| test | `center_crop` |

Use the same mode as training before the DINO image processor (size `448`).

## Test metrics (n=854)

| Metric | Value |
|--------|------:|
| Accuracy | 100.0% |
| Macro F1 | 1.000 |
| AUC-ROC | 1.000 |
| Loss | 0.1237 |

## Training config

| Setting | Value |
|---------|-------|
| Stages | A + B + C |
| Epochs A / B | 5 / 10 |
| LR head A | 0.0005 |
| LR backbone B | 5e-06 |
| LR head B | 5e-05 |
| Unfreeze blocks B | 4 |
| Scheduler | `cosine_warmup` |
| Class weights | `none` |
| Label smoothing | 0.05 |



## Training history

![Training curves](training_history.png)

Per-epoch metrics: `training_history.json` and `results.json` → `history`.

## Files

| File | Description |
|------|-------------|
| `final_model.pt` | Weights + `idx_to_label` + test metrics |
| `results.json` | Full training config, history, report |
| `training_history.json` | Stage A/B epoch logs |
| `confusion_matrix.json` | Machine-readable CM |
| `confusion_matrix.png` | Plot |
| `training_history.png` | Loss / F1 curves |
| `model_card.json` | Summary metadata |
| `config.yaml` | Training hyperparameters (copy) |
| `inference_classifier.py` | CLI inference on image paths |

## Load weights

```python
import torch
ckpt = torch.load("final_model.pt", map_location="cpu", weights_only=False)
print(ckpt["test_metrics"])
print(ckpt["idx_to_label"])
```

## Inference

Use the same preprocessing as training (`center_crop`, size `448`). The bundled script reads defaults from `model_card.json` when flags are omitted.

```bash
pip install -r requirements-inference.txt
python inference_classifier.py --checkpoint final_model.pt --image page.jpg
```

Explicit flags (recommended for reproducibility):

```bash
python inference_classifier.py \
  --checkpoint final_model.pt \
  --image page.jpg \
  --preprocess center_crop \
  --preprocess-size 448
```

**Classes:** `non_flipped` = upright page; `flipped` = upside-down (180° rotation).


## Citation

```bibtex
@misc{bdrcpageorientationmodel,
  title  = {Tibetan Page Orientation Classifier (DINOv3)},
  author = {Buddhist Digital Resource Center and OpenPecha},
  year   = {2026},
  url    = {https://huggingface.co/BDRC/tibetan-page-orieantation-classifier},
  dataset = {https://hunggingface.co/BDRC/tibetan-page-orientation-classifier-dataset},
  experiment = {center-cropped}
  note   = {Trained on BDRC manuscript images}
}
```

## License

**Model weights and inference code:** [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).


## Acknowledgements

All images are provided by the Buddhist Digital Resource Center (BDRC). This dataset was developed by Dharmaduta from specifications provided by BDRC for the project "The BDRC Etext Corpus", with funding from the Khyentse Foundation. **[Buddhist Digital Resource Center](https://www.bdrc.io)** (BDRC). Developed by Dharmaduta / OpenPecha.