Instructions to use aradootle/sam-vit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aradootle/sam-vit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="aradootle/sam-vit-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("aradootle/sam-vit-base") model = AutoModelForMaskGeneration.from_pretrained("aradootle/sam-vit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from aradootle/sam-vit-base: direct link, hf CLI and curl.
- Browser
- Download file 375 MB
-
https://huggingface.co/aradootle/sam-vit-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://aradootle/sam-vit-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/aradootle/sam-vit-base/resolve/main/pytorch_model.bin
375 MB
- Xet hash:
- 4bca31314fefc76ae7c8675d0cbb709608bd5093cc1ab3446b8a8836a981554f
- Size of remote file:
- 375 MB
- SHA256:
- 1a1e860feeb895bc46f704d4faad2a0be739b5dfdca0ebdda520ffbcfb73f348
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