Instructions to use timm/eva02_large_patch14_448.mim_m38m_ft_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/eva02_large_patch14_448.mim_m38m_ft_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/eva02_large_patch14_448.mim_m38m_ft_in1k", pretrained=True) - Transformers
How to use timm/eva02_large_patch14_448.mim_m38m_ft_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/eva02_large_patch14_448.mim_m38m_ft_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 AutoModel model = AutoModel.from_pretrained("timm/eva02_large_patch14_448.mim_m38m_ft_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/eva02_large_patch14_448.mim_m38m_ft_in1k: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/eva02_large_patch14_448.mim_m38m_ft_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/eva02_large_patch14_448.mim_m38m_ft_in1k/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- a8dedfcf9cdf4b4794cdd8953bf5056b564ae39c5534bf1d0db9c6cc97a1333e
- Size of remote file:
- 1.22 GB
- SHA256:
- b1d505cecead0e85996ecf8a734bc54731e867ed4ee94fe59d69a288094ecb15
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