Instructions to use facebook/regnet-y-002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-002") 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-002") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-002", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ec693faec3a08748115f586bfe6fc39b598b854f6eabb59e79447bc24e7ce42
- Size of remote file:
- 12.9 MB
- SHA256:
- d177beab96620b2cab5f324a15071e3a656ce9543407479f8a65ce3d20b8ed01
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