Instructions to use timm/dm_nfnet_f4.dm_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/dm_nfnet_f4.dm_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/dm_nfnet_f4.dm_in1k", pretrained=True) - Transformers
How to use timm/dm_nfnet_f4.dm_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/dm_nfnet_f4.dm_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/dm_nfnet_f4.dm_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54886cb6d06bd57229eac4bf026d8550c4266335c794e36e4e8d52d122d8c729
- Size of remote file:
- 1.26 GB
- SHA256:
- 2a888cfb9a20e0fde412c7bd579b60d54db632450389bf059dd1cf8a83d85ff1
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