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
Change pipeline tag to image-feature-extraction
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by merve HF Staff - opened
README.md
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license: apache-2.0
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library_name: timm
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tags:
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- feature-extraction
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- timm
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---
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# Model card for samvit_base_patch16.sa1b
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license: apache-2.0
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library_name: timm
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tags:
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- image-feature-extraction
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- timm
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---
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# Model card for samvit_base_patch16.sa1b
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