Instructions to use BDRC/6-way-balanced-script-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BDRC/6-way-balanced-script-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BDRC/6-way-balanced-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/6-way-balanced-script-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 529 Bytes
3d37aab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # Split statistics
- **Source:** `local_parquet`
- **Total images:** 36,540
## Images per split
| Split | Total |
|-------|------:|
| train | 32,400 |
| val | 3,600 |
| test | 540 |
## Images per class (per split)
| Class | train | val | test | **All** |
|-------|------:|------:|------:|------:|
| Danyig | 5,400 | 600 | 90 | 6,090 |
| Druma | 5,400 | 600 | 90 | 6,090 |
| Gyuyig | 5,400 | 600 | 90 | 6,090 |
| Pedri | 5,400 | 600 | 90 | 6,090 |
| Tsugdri | 5,400 | 600 | 90 | 6,090 |
| Uchen | 5,400 | 600 | 90 | 6,090 |
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