Instructions to use cvmil/vit-base-patch16-224_augmented-v2_fft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvmil/vit-base-patch16-224_augmented-v2_fft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cvmil/vit-base-patch16-224_augmented-v2_fft") 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("cvmil/vit-base-patch16-224_augmented-v2_fft") model = AutoModelForImageClassification.from_pretrained("cvmil/vit-base-patch16-224_augmented-v2_fft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cvmil/vit-base-patch16-224_augmented-v2_fft: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/cvmil/vit-base-patch16-224_augmented-v2_fft/resolve/main/training_args.bin
- Command line
-
hf download hf://cvmil/vit-base-patch16-224_augmented-v2_fft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cvmil/vit-base-patch16-224_augmented-v2_fft/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- 9fd807e4677139ee5877f9db5c409fa3792bf0bd7db7e418ce70dafb7287aa05
- Size of remote file:
- 5.5 kB
- SHA256:
- d91f12d56a4d2f791cdd3cb7ca4fca171c9aedb1d2d21170d89f7b4bafc29c7a
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