Instructions to use hbjoo/vit-base-beans-demo-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hbjoo/vit-base-beans-demo-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hbjoo/vit-base-beans-demo-v5") 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("hbjoo/vit-base-beans-demo-v5") model = AutoModelForImageClassification.from_pretrained("hbjoo/vit-base-beans-demo-v5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hbjoo/vit-base-beans-demo-v5: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/hbjoo/vit-base-beans-demo-v5/resolve/main/training_args.bin
- Command line
-
hf download hf://hbjoo/vit-base-beans-demo-v5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hbjoo/vit-base-beans-demo-v5/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 4171d5c5cba02f541a7ed8206ffde23919a1ef28d039151fe10fc543644050f1
- Size of remote file:
- 5.11 kB
- SHA256:
- b79cf45f07c45dbab4ff18b48f57877275750a6ff301b5cfec97afd8f2c2ed31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.