Instructions to use timm/resnet50.c2_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet50.c2_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet50.c2_in1k", pretrained=True) - Transformers
How to use timm/resnet50.c2_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet50.c2_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.c2_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from timm/resnet50.c2_in1k: direct link, hf CLI and curl.
- Browser
- Download file 103 MB
-
https://huggingface.co/timm/resnet50.c2_in1k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://timm/resnet50.c2_in1k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/timm/resnet50.c2_in1k/resolve/main/pytorch_model.bin
103 MB
- Xet hash:
- 5613ef8a173155634f0ca0363deca1a7a8c3cc1388c90094b67ea1095b8f32df
- Size of remote file:
- 103 MB
- SHA256:
- 639807b9ed04f3bf93f1cebc939bb7a93d29d8a7295a306b49bd44cacb118f8c
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