Instructions to use timm/dla60.in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/dla60.in1k with timm:
import timm model = timm.create_model("hf_hub:timm/dla60.in1k", pretrained=True) - Transformers
How to use timm/dla60.in1k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="timm/dla60.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/dla60.in1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5dc44e5decfd12ad92300995aa40c8976b50cf6fe35dd14956f5b198f678457a
- Size of remote file:
- 88.4 MB
- SHA256:
- fbf4e531fd1a6e9e1b620b1155f2e8483ec24bf80b54b728b5dd6c55c577dd90
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