Instructions to use Visual-Attention-Network/van-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Visual-Attention-Network/van-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Visual-Attention-Network/van-tiny") 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-tiny", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 496ecbd83544945a296439737098f5d533e066515ea7a1162fd4306860fde27a
- Size of remote file:
- 16.6 MB
- SHA256:
- df40b091c7f2067c8086d24c6fd9b7403ce773593f2a6df02ad035e0de575ec0
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