Instructions to use facebook/regnet-x-120 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-x-120 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-x-120") 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-120") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-x-120", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80451173859a40727540cea5eca60d6971d721234d75bd7c0accc95c3b153255
- Size of remote file:
- 185 MB
- SHA256:
- b0847cc71ed695d663d2ebd6d661a8d2e561c4dd14353fccb605b9b1f696928d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.