Instructions to use Migga/ViT_Chess_10_500k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Migga/ViT_Chess_10_500k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Migga/ViT_Chess_10_500k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Migga/ViT_Chess_10_500k") model = AutoModelForImageClassification.from_pretrained("Migga/ViT_Chess_10_500k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3dbc1c863f45f973278eea7e0f776c61304ab310196b0a22e80167abc9d6907a
- Size of remote file:
- 575 MB
- SHA256:
- 4992533283f550fae985cf5e52441bce432b79adddbaa376b38c236571502690
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