Instructions to use Visual-Attention-Network/van-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Visual-Attention-Network/van-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Visual-Attention-Network/van-base") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("Visual-Attention-Network/van-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da038ec2e5e1c0f0434954f56548095006597cd00dc0069360f0f937dc78ba8f
- Size of remote file:
- 107 MB
- SHA256:
- 2156b5ed7481526b63f7b3b5a086960d44565285962c1eb488e194199a098434
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