Instructions to use facebook/sam-vit-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/sam-vit-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="facebook/sam-vit-large")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("facebook/sam-vit-large") model = AutoModelForMaskGeneration.from_pretrained("facebook/sam-vit-large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9d7fa6208172031088a508670f88c832b1c901ea4540a3bcc641aa541c96504e
- Size of remote file:
- 1.25 GB
- SHA256:
- 149bba0bfe0b10f856adb815c37000978ceda04ed3a373c54e565645ae6b7c53
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