Instructions to use timm/mobilenetv4_conv_medium.e500_r224_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/mobilenetv4_conv_medium.e500_r224_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/mobilenetv4_conv_medium.e500_r224_in1k", pretrained=True) - Transformers
How to use timm/mobilenetv4_conv_medium.e500_r224_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/mobilenetv4_conv_medium.e500_r224_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/mobilenetv4_conv_medium.e500_r224_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/mobilenetv4_conv_medium.e500_r224_in1k: direct link, hf CLI and curl.
- Browser
- Download file 39.3 MB
-
https://huggingface.co/timm/mobilenetv4_conv_medium.e500_r224_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/mobilenetv4_conv_medium.e500_r224_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/mobilenetv4_conv_medium.e500_r224_in1k/resolve/main/pytorch_model.bin
39.3 MB
- Xet hash:
- 5e466dda054a11be6958dd8ff149130512cc263b3183c0295c1ee5630c591a0c
- Size of remote file:
- 39.3 MB
- SHA256:
- 3d8f59362f41bc4566e258373cba319c8f79f4ed65c1fb7bda76bb080ae19101
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