Instructions to use timm/lambda_resnet50ts.a1h_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/lambda_resnet50ts.a1h_in1k with timm:
import timm model = timm.create_model("hf_hub:timm/lambda_resnet50ts.a1h_in1k", pretrained=True) - Transformers
How to use timm/lambda_resnet50ts.a1h_in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/lambda_resnet50ts.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/lambda_resnet50ts.a1h_in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b5eeac66caa26c5a6c263c3457f167f176878e045be957531444d41e7c14712
- Size of remote file:
- 86.5 MB
- SHA256:
- aa21c2821d9d6fa9c5af9629e78b54dab1b47a4f61497ed728fc7759e4a0fbc1
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