Instructions to use timm/lambda_resnet26t.c1_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/lambda_resnet26t.c1_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/lambda_resnet26t.c1_in1k", pretrained=True) - Transformers
How to use timm/lambda_resnet26t.c1_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/lambda_resnet26t.c1_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/lambda_resnet26t.c1_in1k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bcac4b044d583588be546b2bf2c729242693f19a5f4c8ed6e0c2f68322bd347a
- Size of remote file:
- 44 MB
- SHA256:
- f9431a5022565c624d4a02ded50bac02314f7a5b4948112392ba5ee57a6e4fd9
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