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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfbd79788f02a5115cbb890d730ebf08ffd02b53a2e202f52d74223bc595461d
- Size of remote file:
- 1.02 GB
- SHA256:
- 884791d18d7c80c34315d34f9944078a5d9f6da35d7c98af1ed46a25f73fbe70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.