Instructions to use timm/davit_base.msft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/davit_base.msft_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/davit_base.msft_in1k", pretrained=True) - Transformers
How to use timm/davit_base.msft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/davit_base.msft_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/davit_base.msft_in1k") model = AutoModelForImageClassification.from_pretrained("timm/davit_base.msft_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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transformers tag
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Adding `safetensors` variant of this model (#2)
Verified signature- Adding `safetensors` variant of this model (552243697ed84c024092ba1ee545b79c777f20bd) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>8920486
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Fix library_name.
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initial commit
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