Instructions to use CollectionStudio/sam2-hiera-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/sam2-hiera-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="CollectionStudio/sam2-hiera-tiny")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("CollectionStudio/sam2-hiera-tiny") model = AutoModel.from_pretrained("CollectionStudio/sam2-hiera-tiny") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dba1ece6b5f805c8c08616c69f8f4ff62689b1211ae2f8a8cd77c3739296f676
- Size of remote file:
- 156 MB
- SHA256:
- 65b50056e05bcb13694174f51bb6da89c894b57b75ccdf0ba6352c597c5d1125
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.