Instructions to use timm/resnetv2_50d_evos.ah_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnetv2_50d_evos.ah_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnetv2_50d_evos.ah_in1k", pretrained=True) - Transformers
How to use timm/resnetv2_50d_evos.ah_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnetv2_50d_evos.ah_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/resnetv2_50d_evos.ah_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edcbf998d995cf820349fe1a7c26116350dbb1b6c45872353b4d777975d83001
- Size of remote file:
- 102 MB
- SHA256:
- 712196929a71a2107a7fbaf4a2e45df75f9ad0ef47ee8c6dac7247196ff56039
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