Instructions to use hiroaki-f/binary_label_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hiroaki-f/binary_label_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hiroaki-f/binary_label_classification") 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("hiroaki-f/binary_label_classification") model = AutoModelForImageClassification.from_pretrained("hiroaki-f/binary_label_classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cb0582489d2737cf12f5667a4cafc7c2d818689ceb3999194a6718b8ad2dbc3
- Size of remote file:
- 343 MB
- SHA256:
- 12b01cb205cfa8e84f58c96c3fc05d92fec38134de2657b1e8a130e39f54f79e
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