Instructions to use andreotte/vit-multi-label with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andreotte/vit-multi-label with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="andreotte/vit-multi-label") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("andreotte/vit-multi-label") model = AutoModelForImageClassification.from_pretrained("andreotte/vit-multi-label", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from andreotte/vit-multi-label: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/andreotte/vit-multi-label/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://andreotte/vit-multi-label/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/andreotte/vit-multi-label/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- b3e0017136f918fc2fba89b79dba1d8d2e0f049c38087bb854776a2c30ca2146
- Size of remote file:
- 343 MB
- SHA256:
- 5f50b8a4074ea5f9bb9afbb54fc9f2030c3975cbc7851715f9de9055de70d35d
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