Image Classification
Transformers
PyTorch
TensorBoard
vit
other-image-classification
Generated from Trainer
Eval Results (legacy)
Instructions to use nateraw/vit-base-beans-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/vit-base-beans-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/vit-base-beans-demo") 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("nateraw/vit-base-beans-demo") model = AutoModelForImageClassification.from_pretrained("nateraw/vit-base-beans-demo") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Update Hugging Face dataset ID
#4 opened about 2 years ago
by
librarian-bot
Librarian Bot: Add base_model information to model
#3 opened over 2 years ago
by
librarian-bot
Adding `safetensors` variant of this model
#2 opened over 2 years ago
by
SFconvertbot
Add evaluation results on beans dataset
#1 opened almost 4 years ago
by
autoevaluator