Image Classification
Transformers
Tibetan
dinov3
tibetan
script-classification
paleography
fine-tuned
document-analysis
Eval Results (legacy)
Instructions to use openpecha/tibetan-script-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openpecha/tibetan-script-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="openpecha/tibetan-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("openpecha/tibetan-script-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Class distribution (`whole_page` split totals)
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The whole-page split has
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The per-class table below sums **train + val + test** counts. A benchmark exclusion manifest (`benchmark_page_ids.json`, **88** page IDs across 18 classes) is consulted when building splits: any file whose page ID matches is skipped and counted in `splits.json` under `skipped_excluded_files_by_class`. For the published `whole_page/splits.json`, that skip map is **empty**—so either those pages were **not present** under the training `data_dir`, or they had **already been removed** before this split was built. The **5,684** totals are whatever files remained for stratified splitting.
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## Class distribution (`whole_page` split totals)
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The whole-page split has 5,684 samples in total (train 3,996 / val 844 / test 844). 88 benchmark images (5 per class) were excluded from training and evaluation.
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