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:
- a97d80e6e6d1a4bc46be76f004867e0ad42ea7a4f9bd6497be5409dff66935ba
- Size of remote file:
- 4.86 kB
- SHA256:
- ae4d45c951adf970968ec6c1bb42420d61e06e215c0700862d045548c605d4c8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.