Instructions to use facebook/regnet-x-320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-x-320 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-x-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-x-320") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-x-320", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 053a709106900bce2b8ea1d6543980e428b618da8674f299d2dfd6a281796167
- Size of remote file:
- 432 MB
- SHA256:
- 4739e48300b2e7d8a6c2304da92b6f35749e2bacea34549dfc3e0a040fc069ad
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