Instructions to use facebook/regnet-x-080 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-x-080 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-x-080") 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-080") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-x-080", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights
Joao GanteModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=5.102e-04; Maximum converted output difference=5.102e-04. cc @patrickvonplaten [HF maintainer(s) for this repo]4edc4a5
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Upload README.md
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Add feature extractor
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Add model
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initial commit
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