Instructions to use timm/ecaresnet50t.a1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/ecaresnet50t.a1_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/ecaresnet50t.a1_in1k", pretrained=True) - Transformers
How to use timm/ecaresnet50t.a1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/ecaresnet50t.a1_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/ecaresnet50t.a1_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e32b4d49a1a460069c0de8a4c237f09ecca12cb24f8986ee6af813d4a50142c0
- Size of remote file:
- 103 MB
- SHA256:
- 7b6d59dfc9c7eaef29d34c550944d951d33bfb5f15311e322c953164a7b3da90
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