Instructions to use BDRC/gyuyig-tsugdri-binary-script-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BDRC/gyuyig-tsugdri-binary-script-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BDRC/gyuyig-tsugdri-binary-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/gyuyig-tsugdri-binary-script-classifier", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Split statistics | |
| - **Source:** `hf` | |
| - **Total images:** 402 | |
| ## Images per split | |
| | Split | Total | | |
| |-------|------:| | |
| | train | 342 | | |
| | val | 60 | | |
| | test | 0 | | |
| ## Images per class (per split) | |
| | Class | train | val | test | **All** | | |
| |-------|------:|------:|------:|------:| | |
| | Gyuyig | 171 | 30 | 0 | 201 | | |
| | Tsugdri | 171 | 30 | 0 | 201 | | |