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 pytorch_model.bin from socokal/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/socokal/vit-base-beans/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://socokal/vit-base-beans/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/socokal/vit-base-beans/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 5fb2f528da71e7114e9987637d69de8074c8d393d5bb0f980e67c6955cbec156
- Size of remote file:
- 343 MB
- SHA256:
- 9eaccd34af0b8841e6fb371c6f33d77f16dd29158f8fce2088f8985564ea46f0
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