Instructions to use cvmil/beit-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/beit-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/beit-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/beit-base-patch16-224_augmented-v2_fft") model = AutoModelForImageClassification.from_pretrained("cvmil/beit-base-patch16-224_augmented-v2_fft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cvmil/beit-base-patch16-224_augmented-v2_fft: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/cvmil/beit-base-patch16-224_augmented-v2_fft/resolve/main/training_args.bin
- Command line
-
hf download hf://cvmil/beit-base-patch16-224_augmented-v2_fft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cvmil/beit-base-patch16-224_augmented-v2_fft/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- ffae5073b48ba7180f9002cd2db14ae23ee875284166608b3bdb3ce8038ee6c7
- Size of remote file:
- 5.5 kB
- SHA256:
- 85fb9e50fba096f23c042b5d8a30ff09721c4c73de8288cdc7174ceaecf60197
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