Image Classification
Transformers
PyTorch
TensorFlow
Safetensors
data2vec-vision
image-feature-extraction
vision
Instructions to use facebook/data2vec-vision-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-vision-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/data2vec-vision-large") 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-large") model = AutoModel.from_pretrained("facebook/data2vec-vision-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add TensorFlow weights
Browse files- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:b0ca6b615ab2171e24aa7062a31924a1acb2ab5434afdbeadb2231e38780fcd0
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size 1213114000
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