Instructions to use facebook/regnet-y-320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-320 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-320") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/regnet-y-320") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-320") - Notebooks
- Google Colab
- Kaggle
Add TF weights (#1)
Browse files- Add TF weights (6fa9a4d24ba517d976996658e63ad2971515c9e6)
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:317a1e37f8f0ca714a3fa1ee04f07ea7d27764d5edba4774949a65dedf8c1939
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size 581325168
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