Instructions to use abletobetable/image_feature_extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abletobetable/image_feature_extractor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="abletobetable/image_feature_extractor") 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("abletobetable/image_feature_extractor") model = AutoModelForImageClassification.from_pretrained("abletobetable/image_feature_extractor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a4f7a1392eb12182cdf4b5c564751ef20aa125df21cc9fe8a8a35485d8569ef
- Size of remote file:
- 350 MB
- SHA256:
- 483220676ba5305c49be2c30c07122937cb282a132de297f29c6f663bf6739b5
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