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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/dm_nfnet_f3.dm_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 135 Bytes
fd39b05 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:884791d18d7c80c34315d34f9944078a5d9f6da35d7c98af1ed46a25f73fbe70
size 1019779668
|