Instructions to use facebook/regnet-x-002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-x-002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-x-002") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("facebook/regnet-x-002") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-x-002", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights (#1)
- Add TF weights (77bbc1603ef3589aa246ae8fa7a42c811b18f890) Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>2b69d3a
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Upload README.md
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Add feature extractor
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initial commit
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