Instructions to use facebook/data2vec-vision-large-ft1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/data2vec-vision-large-ft1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/data2vec-vision-large-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-large-ft1k") model = AutoModelForImageClassification.from_pretrained("facebook/data2vec-vision-large-ft1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59a56cf657c51f2c09a12c52de0d00f49ea5991cd1954011dfc3a156c9c423e7
- Size of remote file:
- 1.23 GB
- SHA256:
- d74ca2892b33817287ee34965f53826109740e2f879717ff8d6f2d9eb9d7283a
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