Instructions to use facebook/regnet-y-064 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-064 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-064") 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-064") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-064", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0c094785d95a4324d45319c7faf6452654a34872c6eb082cca2f1539dc89d82
- Size of remote file:
- 123 MB
- SHA256:
- 30d8cd4d4c86e0e72c8e0c9793a163c8d199281956e143acc92232faa5c998c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.