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")# 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
- Xet hash:
- 97a3e09c4c1c86456fc0f1959ace351e5c94fc42e613c761d33b54ff94236cfd
- Size of remote file:
- 10.9 MB
- SHA256:
- 87f1976aaefc3d92d55a01f4ba64a00940e69e3f7ede6d4f39d6a6f16c741114
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.