Instructions to use timm/resnet50.a1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet50.a1_in1k with timm:
import timm model = timm.create_model("hf-hub:timm/resnet50.a1_in1k", pretrained=True) - Transformers
How to use timm/resnet50.a1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet50.a1_in1k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/resnet50.a1_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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import torch in first snippet (#2)
Verified signature- import torch in first snippet (5b59b5dc288b8cf44f9649aaa5a9115712193014) Co-authored-by: Pedro Cuenca <pcuenq@users.noreply.huggingface.co>7672686
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transformers tag
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Fix library_name.
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initial commit
Ross Wightmancdb350e