Instructions to use timm/dla46_c.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/dla46_c.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/dla46_c.in1k", pretrained=True) - Transformers
How to use timm/dla46_c.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/dla46_c.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/dla46_c.in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3da06cb8a792d2b3472141729e5cc7f17eae65b449a0140a456a04405ed9aff
- Size of remote file:
- 5.27 MB
- SHA256:
- e1d63d8a64fe92b61f167ecb2d2379833c429b452fe685a30c03748c2f8dcb1c
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