Instructions to use timm/cspresnet50.ra_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/cspresnet50.ra_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/cspresnet50.ra_in1k", pretrained=True) - Transformers
How to use timm/cspresnet50.ra_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/cspresnet50.ra_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/cspresnet50.ra_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01c1f788862c8acbad3cf86498127ff9e624db58fb1a2a9ed9f683c8ec174ee4
- Size of remote file:
- 86.7 MB
- SHA256:
- f29e438fc7f5d3063ac66915d8528b3438345078008d8943c2fe3c88c2253cb1
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