Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use nickmuchi/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickmuchi/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nickmuchi/vit-base-beans") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nickmuchi/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("nickmuchi/vit-base-beans") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c044e365f7d9b439edf8226c6fc6c2e6cfaa4f17f5364324d5f7b7f9c640a236
- Size of remote file:
- 343 MB
- SHA256:
- 64d0cf82b1074ff8147127be1010120f13f8472474973864d146b510a431f432
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.