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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13ea99c962ff52f8bdd6bcb807152a346635ca197f07e8c83fcb001d13270ca5
- Size of remote file:
- 581 MB
- SHA256:
- 401c0c6f40e1b0c5ee209ae758195330ecf2264dc0d1fd157f390bc31792afa9
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