Instructions to use timm/resnet50.tv2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet50.tv2_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet50.tv2_in1k", pretrained=True) - Transformers
How to use timm/resnet50.tv2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet50.tv2_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/resnet50.tv2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b8fbbe015da308be24b426046c6aff1a2bacad32fcaabd6c6756c822cefe2e4
- Size of remote file:
- 103 MB
- SHA256:
- 2ecb663569ff7be76f460dd9e52a550ad9185c05cbeb5baa5c67905c2a952bf0
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