Image Classification
Transformers
PyTorch
TensorBoard
vit
vision
Generated from Trainer
Eval Results (legacy)
Instructions to use stlxx/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stlxx/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="stlxx/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("stlxx/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("stlxx/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from stlxx/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 64.1 kB
-
https://huggingface.co/stlxx/vit-base-beans/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://stlxx/vit-base-beans/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/stlxx/vit-base-beans/resolve/main/pytorch_model.bin
64.1 kB
- Xet hash:
- f461723a005e067e32e216217601c05372d56b41f1a0ce349a392a122d39d945
- Size of remote file:
- 64.1 kB
- SHA256:
- 894f7688c15a12f431d2b103c55aeee209705541dcf923004c29bcb164118c9d
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