Instructions to use facebook/regnet-y-160 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-160 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-160") 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-160") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-160", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add TF weights (#1)
Browse files- Add TF weights (f922d5154aee43802b40b390dff36db38602a13a)
Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>
- 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 sha256:257b67e78ab5b2adbbd76a0382a69b806a92807bef9bb2e36696f6faf5aaf1be
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size 335318120
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