Instructions to use timm/dm_nfnet_f0.dm_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/dm_nfnet_f0.dm_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/dm_nfnet_f0.dm_in1k", pretrained=True) - Transformers
How to use timm/dm_nfnet_f0.dm_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/dm_nfnet_f0.dm_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 AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("timm/dm_nfnet_f0.dm_in1k") model = AutoModelForImageClassification.from_pretrained("timm/dm_nfnet_f0.dm_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/dm_nfnet_f0.dm_in1k: direct link, hf CLI and curl.
- Browser
- Download file 286 MB
-
https://huggingface.co/timm/dm_nfnet_f0.dm_in1k/resolve/ca8e830961c2ebceffff7affff34acfe7a1da673/pytorch_model.bin
- Command line
-
hf download hf://timm/dm_nfnet_f0.dm_in1k@ca8e830961c2ebceffff7affff34acfe7a1da673/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/dm_nfnet_f0.dm_in1k/resolve/ca8e830961c2ebceffff7affff34acfe7a1da673/pytorch_model.bin
286 MB
- Xet hash:
- c4edd8c7dca48208aa443b0b9157426e68c1819e4c64852afe81d0a09423ba68
- Size of remote file:
- 286 MB
- SHA256:
- 6389dc8736b6d074b6071121b196ff78441dd024ecc8ec0f319245ae4ebe96d3
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