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")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("facebook/data2vec-vision-base") model = AutoModel.from_pretrained("facebook/data2vec-vision-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b322caac3ce665ebe940f481d9943a913b057f2c4ae5664646776e88d785592e
- Size of remote file:
- 343 MB
- SHA256:
- 506e344b373f3dc491c4fc7bb4bafeb405c1708ee6038350e16c25a2e2d8d142
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