Instructions to use bn22/my-timm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use bn22/my-timm-model with timm:
import timm model = timm.create_model("hf_hub:bn22/my-timm-model", pretrained=True) - Transformers
How to use bn22/my-timm-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bn22/my-timm-model", device_map="auto") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bn22/my-timm-model", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04190ddb852e74451a80356ec864b69ce94278b722fd15054bcd698e4a9ca738
- Size of remote file:
- 93.9 MB
- SHA256:
- 8309976b41626d66016ce2d11bf728ed5266184d3f0cd9ccbd95fb899d9d9e1b
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