Instructions to use timm/densenet121.ra_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/densenet121.ra_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/densenet121.ra_in1k", pretrained=True) - Transformers
How to use timm/densenet121.ra_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/densenet121.ra_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.ra_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 157010b767a1cd970296654587e6c7976f87b2ff746039eeb56656996711c015
- Size of remote file:
- 32.5 MB
- SHA256:
- e73d2a763b3420ca9fb0ec8e45e094e54d8e1e3add4b2e5073da7d38f0be4613
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