Instructions to use Augusto777/vit-base-patch16-224-RU5-40-8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Augusto777/vit-base-patch16-224-RU5-40-8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Augusto777/vit-base-patch16-224-RU5-40-8") 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("Augusto777/vit-base-patch16-224-RU5-40-8") model = AutoModelForImageClassification.from_pretrained("Augusto777/vit-base-patch16-224-RU5-40-8", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Augusto777/vit-base-patch16-224-RU5-40-8: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/Augusto777/vit-base-patch16-224-RU5-40-8/resolve/main/training_args.bin
- Command line
-
hf download hf://Augusto777/vit-base-patch16-224-RU5-40-8/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Augusto777/vit-base-patch16-224-RU5-40-8/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 99c1f871081036ea5a3c74491adb069b5e8702a25b62505c00cd4188b89284f0
- Size of remote file:
- 4.73 kB
- SHA256:
- 6b3376c6b314220f2d6ceedcd35cb2aee41f34faae7678797ede3a79e365a5d6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.