Instructions to use thenewsupercell/DF_Image_VIT_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thenewsupercell/DF_Image_VIT_V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="thenewsupercell/DF_Image_VIT_V1") 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("thenewsupercell/DF_Image_VIT_V1") model = AutoModelForImageClassification.from_pretrained("thenewsupercell/DF_Image_VIT_V1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from thenewsupercell/DF_Image_VIT_V1: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/thenewsupercell/DF_Image_VIT_V1/resolve/main/training_args.bin
- Command line
-
hf download hf://thenewsupercell/DF_Image_VIT_V1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/thenewsupercell/DF_Image_VIT_V1/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- dd770b7a0f61f48c391689afafedaab68641cfdda30b9962a34f75dd08f2837b
- Size of remote file:
- 5.3 kB
- SHA256:
- 06aeafc6e774ae29928ca26839ff01515046e7eb4bd0290cafd7780743921ea6
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