Instructions to use timm/test_convnext3.r160_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/test_convnext3.r160_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/test_convnext3.r160_in1k", pretrained=True) - Transformers
How to use timm/test_convnext3.r160_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/test_convnext3.r160_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/test_convnext3.r160_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e183f1ef00fa171b92652eb6db8439fea5017c371583c45844a1af3df664575
- Size of remote file:
- 1.9 MB
- SHA256:
- 6f6de631cdf6f30c777d74e7b99d6f743016cf3577d1e37f3e9be81fdb282c52
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