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:
- 6df6193c7c909337043abb722d378ec74cda84ea3bcc665b0098717bc0f8c61f
- Size of remote file:
- 3.52 kB
- SHA256:
- 94e70cc0beb68193de9a1ead770b64801a177535e56eaf1d9d86449595882417
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