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
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 |