Instructions to use google/bit-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/bit-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/bit-50") 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("google/bit-50") model = AutoModelForImageClassification.from_pretrained("google/bit-50") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd6cc7d515e50fd1b15316f1e58ca103b6cef22e8b3fa2ea08a05b00ce7db2e1
- Size of remote file:
- 102 MB
- SHA256:
- 2e1d030d1667e64f451d7e81f768f7a028b68402fc5318325f4fd66727b6d7dc
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