Instructions to use tiya1012/vit-base-beans-55 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/vit-base-beans-55 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tiya1012/vit-base-beans-55") 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("tiya1012/vit-base-beans-55") model = AutoModelForImageClassification.from_pretrained("tiya1012/vit-base-beans-55", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/vit-base-beans-55: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/tiya1012/vit-base-beans-55/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/vit-base-beans-55/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/vit-base-beans-55/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 81e64772c7403ea8095d2291bf1df1c138e43482d2d999bab486d76e0b5b7113
- Size of remote file:
- 343 MB
- SHA256:
- e9368301b73b87d6a5319254beee281256d98c12d92ab12c384ef2cb7216adc9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.