Instructions to use timm/dm_nfnet_f3.dm_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/dm_nfnet_f3.dm_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/dm_nfnet_f3.dm_in1k", pretrained=True) - Transformers
How to use timm/dm_nfnet_f3.dm_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/dm_nfnet_f3.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_f3.dm_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d7130d3760f8e1c72c826d08045f7a4f6de3d49aee2f5d127583b7bae45aea0b
- Size of remote file:
- 1.02 GB
- SHA256:
- b7c37427b81f7bca7bb2812399873328b6582e5f9d0b43163f9e76059bd54bef
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