Instructions to use andreotte/vit-multi-label-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andreotte/vit-multi-label-poc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="andreotte/vit-multi-label-poc") 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("andreotte/vit-multi-label-poc") model = AutoModelForImageClassification.from_pretrained("andreotte/vit-multi-label-poc") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
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version https://git-lfs.github.com/spec/v1
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oid sha256:9465f3cdd637da70cc506e8f104d399d976f5aac335915e7185c94b9a5e0e04a
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size 343236280
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