Instructions to use JeffrinSam/SAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeffrinSam/SAM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="JeffrinSam/SAM")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("JeffrinSam/SAM") model = AutoModelForMaskGeneration.from_pretrained("JeffrinSam/SAM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f7fd15b391c2b4bfa35718991e5073c8b5f3ec41f37db99842a043d32e853e7
- Size of remote file:
- 375 MB
- SHA256:
- 8de00f7794b2ee81e16fc5da2482de2deca9c2b7e3dc9e7bd4b813cc7f3eb317
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