Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use sjdata/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sjdata/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sjdata/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("sjdata/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("sjdata/vit-base-beans") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 4.0, | |
| "total_flos": 3.205097416476426e+17, | |
| "train_loss": 0.13431097194552422, | |
| "train_runtime": 68.8344, | |
| "train_samples_per_second": 60.086, | |
| "train_steps_per_second": 3.777 | |
| } |