Instructions to use timm/samvit_base_patch16.sa1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/samvit_base_patch16.sa1b with timm:
import timm model = timm.create_model("hf_hub:timm/samvit_base_patch16.sa1b", pretrained=True) - Transformers
How to use timm/samvit_base_patch16.sa1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/samvit_base_patch16.sa1b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/samvit_base_patch16.sa1b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be64305829918149ac8a4939c98a4d0411a8b0153b697f3d804a2e27fd351bd0
- Size of remote file:
- 359 MB
- SHA256:
- 6070d2b98dcedcf3626ad96b3c0aef52c70842bc2498d13eaf7acd686802f1af
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