Instructions to use timm/resnet50.a1h_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/resnet50.a1h_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/resnet50.a1h_in1k", pretrained=True) - Transformers
How to use timm/resnet50.a1h_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/resnet50.a1h_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.a1h_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8009406e2aec27c283bfb071864311acb0fdc77956b9c4abdfdd9c65c071fe4
- Size of remote file:
- 103 MB
- SHA256:
- f0b39c2b17769397d2049ce0587dacd29dde71d2f2a0b7a7198d18db4692fc11
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