Instructions to use timm/densenet201.tv_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/densenet201.tv_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/densenet201.tv_in1k", pretrained=True) - Transformers
How to use timm/densenet201.tv_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/densenet201.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/densenet201.tv_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a26dbd92aa41aaefe55d2652224db2d01e6803c1ea495ed7f7a33fb41d2e9bbb
- Size of remote file:
- 81.1 MB
- SHA256:
- 0f5db56213cd09ba5b4c53ddcdab0ae1c9a11bbd8fc07f0da71b0c099b2fd971
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.