Instructions to use timm/nextvit_base.bd_in1k_384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/nextvit_base.bd_in1k_384 with timm:
import timm model = timm.create_model("hf-hub:timm/nextvit_base.bd_in1k_384", pretrained=True) - Transformers
How to use timm/nextvit_base.bd_in1k_384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/nextvit_base.bd_in1k_384") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/nextvit_base.bd_in1k_384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/nextvit_base.bd_in1k_384: direct link, hf CLI and curl.
- Browser
- Download file 180 MB
-
https://huggingface.co/timm/nextvit_base.bd_in1k_384/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/nextvit_base.bd_in1k_384/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/nextvit_base.bd_in1k_384/resolve/main/pytorch_model.bin
180 MB
- Xet hash:
- 9102a7a19f4a51f22481faa46a4e375583d58b08df4d9d5a33b554fa3138e9af
- Size of remote file:
- 180 MB
- SHA256:
- e3f9166bbb1c87a8036d7f32aadfa37a7a6490978f574862994edb83e3770e33
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