Instructions to use timm/davit_tiny.msft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/davit_tiny.msft_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/davit_tiny.msft_in1k", pretrained=True) - Transformers
How to use timm/davit_tiny.msft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/davit_tiny.msft_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/davit_tiny.msft_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model (#1)
Browse files- Adding `safetensors` variant of this model (31cc79a113d1f7f69164167156fa79a3b26161fa)
Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:085937207d2e51c1d1e0a3ee5f3156994cce718dc02aef7fa8cec502784adda8
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size 113462256
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