Instructions to use socokal/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use socokal/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="socokal/vit-base-beans") 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("socokal/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("socokal/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from socokal/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/socokal/vit-base-beans/resolve/main/training_args.bin
- Command line
-
hf download hf://socokal/vit-base-beans/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/socokal/vit-base-beans/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- 1ff0b8fa0e8fbe90b5cea90a9e3aaa2fede3cffdf6ef9f9079a6f986236eac80
- Size of remote file:
- 3.31 kB
- SHA256:
- 90f34cac99447a8e3350dc7643b114ddcabf39003ad67318a60a0b3e72b31733
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