Instructions to use timm/densenet121.tv_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/densenet121.tv_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/densenet121.tv_in1k", pretrained=True) - Transformers
How to use timm/densenet121.tv_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/densenet121.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/densenet121.tv_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38f24d7c48fd0df01261a2db8967ce6001e1d50c044f4f5b3d4851210825626c
- Size of remote file:
- 32.3 MB
- SHA256:
- c894c6d9caa317a8ca1942986dee7a16a86c77734a4d691d2abe05389cfef358
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