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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8962e87892635f03c35a4fde59b304481d7761b025951ff1e8f32f382bd082a
- Size of remote file:
- 114 MB
- SHA256:
- 8e5dd07e74925c9cc3831bb0f2acb06aabbb39b918ce93597e741f61f6325753
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