Instructions to use CollectionStudio/sam2-hiera-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/sam2-hiera-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="CollectionStudio/sam2-hiera-small")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("CollectionStudio/sam2-hiera-small") model = AutoModel.from_pretrained("CollectionStudio/sam2-hiera-small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 44850d721fbb56bba96469a4e531680e643a8e31ba391b0af2b222e9a3738ad5
- Size of remote file:
- 184 MB
- SHA256:
- 293ad59be7669df6aa41ba79efcdeb7490f43cdc4c06cee4bf66bd89dafad293
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.