Instructions to use facebook/regnet-y-040 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-040 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-040") 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-y-040") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-040", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Flax Implementation https://github.com/huggingface/transformers/pull/21867 (#2)
- Flax Implementation https://github.com/huggingface/transformers/pull/21867 (d447aff06b00a3f84f5a25b42590b994933a8a70) Co-authored-by: Shubhamai <Shubhamai@users.noreply.huggingface.co>29e70d2
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Update README.md
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Add TF weights (#1)
- Add TF weights (312839fc3541ccf0416583a6dc92b5b774e0ee5b) Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>18798f9
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Upload README.md
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Add model
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Add feature extractor
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Add model
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initial commit
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