Instructions to use dthomas84/refigerator_items with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dthomas84/refigerator_items with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dthomas84/refigerator_items") 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("dthomas84/refigerator_items") model = AutoModelForImageClassification.from_pretrained("dthomas84/refigerator_items", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff304054250c745bde18bffd09405d36fadb2738d7ef0aa6b13ef8f7f620b3ff
- Size of remote file:
- 343 MB
- SHA256:
- ecefc29e0bdd29a3066eabb3fbaa983c05d8a94fd356f31aa2d8160e38d1df7b
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