Image Classification
Transformers
PyTorch
TensorFlow
data2vec-vision
image-feature-extraction
vision
Instructions to use facebook/data2vec-vision-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-vision-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/data2vec-vision-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/data2vec-vision-base") model = AutoModel.from_pretrained("facebook/data2vec-vision-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/data2vec-vision-base: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/facebook/data2vec-vision-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/data2vec-vision-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/data2vec-vision-base/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- b29217d2a620db976b7f8dd367c7176333ceede699cbcc6d46d4967d6c0c2d67
- Size of remote file:
- 343 MB
- SHA256:
- e1942d45a5003da3053504a1d9a7e12fcf70c0e90f2ee9a2b9c7553ba643acfd
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