Instructions to use wangzeze/V2-SAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wangzeze/V2-SAM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="wangzeze/V2-SAM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wangzeze/V2-SAM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc2dcbc5251e7e32ddb2e11e3a523ed861e49aa6f483e3dd38c491bed18b7f17
- Size of remote file:
- 1.33 GB
- SHA256:
- 1852df7d8aa6bc53f15fb05d129105131a67d53f49ca92705639ee32119dc74f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.