Instructions to use timm/densenet161.tv_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/densenet161.tv_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/densenet161.tv_in1k", pretrained=True) - Transformers
How to use timm/densenet161.tv_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/densenet161.tv_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/densenet161.tv_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd58230542bbc78d389201efb48817a71515c4e81cd70648b78df237fafe50d6
- Size of remote file:
- 116 MB
- SHA256:
- a84032cb6405dc38f0b92f2363a6b13fcf326e4df4ea4698fc58be66061da90f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.