Instructions to use timm/densenet169.tv_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/densenet169.tv_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/densenet169.tv_in1k", pretrained=True) - Transformers
How to use timm/densenet169.tv_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/densenet169.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/densenet169.tv_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6330a14520a205e9fb92f696d3170c1cd45b1b568b17af1754bab10079ba315b
- Size of remote file:
- 57.4 MB
- SHA256:
- e982149feee635eebfc55d68b2fa623a59450d08f03b8d9a19205056366e37f0
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