Instructions to use timm/levit_conv_256.fb_dist_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/levit_conv_256.fb_dist_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/levit_conv_256.fb_dist_in1k", pretrained=True) - Transformers
How to use timm/levit_conv_256.fb_dist_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/levit_conv_256.fb_dist_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/levit_conv_256.fb_dist_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/levit_conv_256.fb_dist_in1k: direct link, hf CLI and curl.
- Browser
- Download file 76 MB
-
https://huggingface.co/timm/levit_conv_256.fb_dist_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/levit_conv_256.fb_dist_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/levit_conv_256.fb_dist_in1k/resolve/main/pytorch_model.bin
76 MB
- Xet hash:
- efe6fc76a215b207df08ea0772d3a2dd92d222514bcae4638fa851686aa3ecde
- Size of remote file:
- 76 MB
- SHA256:
- ffb7082b47936bb11f23ae1c6e2dbeffbd59b9d4c9586211c16fe5a934b43fee
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