Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use derhuli/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derhuli/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="derhuli/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("derhuli/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("derhuli/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from derhuli/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/derhuli/vit-base-beans/resolve/main/training_args.bin
- Command line
-
hf download hf://derhuli/vit-base-beans/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/derhuli/vit-base-beans/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- fef0fc3e91de83c4eaf1407efb2fb62045315e494e7e0f82095209382b193e59
- Size of remote file:
- 3.5 kB
- SHA256:
- a74f2c43ae72b065625d93f9cb63de7dcac0f198ac61812f1d90d06a3fe5c4bf
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