Instructions to use timm/vit_base_patch32_224.orig_in21k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_base_patch32_224.orig_in21k with timm:
import timm model = timm.create_model("hf_hub:timm/vit_base_patch32_224.orig_in21k", pretrained=True) - Transformers
How to use timm/vit_base_patch32_224.orig_in21k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_base_patch32_224.orig_in21k")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_base_patch32_224.orig_in21k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Change pipeline tag to image-feature-extraction
Browse files
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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- timm
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- feature-extraction
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datasets:
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- imagenet-21k
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---
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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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datasets:
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- imagenet-21k
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---
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