Instructions to use facebook/data2vec-vision-base-ft1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-vision-base-ft1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/data2vec-vision-base-ft1k") 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("facebook/data2vec-vision-base-ft1k") model = AutoModelForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dec7b0dd5f55edf485f8573e2a5e4f0397f5d6fa6821e17bd51e6b266474254
- Size of remote file:
- 350 MB
- SHA256:
- a7e1a41879c41a068614df16cb82d519587ddb2bfdb581d0956ba64fc6d5240b
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