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
| { | |
| "epoch": 8.0, | |
| "eval_accuracy": 0.9849624060150376, | |
| "eval_loss": 0.05050145834684372, | |
| "eval_runtime": 1.4955, | |
| "eval_samples_per_second": 88.935, | |
| "eval_steps_per_second": 11.368, | |
| "total_flos": 6.410194832952852e+17, | |
| "train_loss": 0.08019667567255405, | |
| "train_runtime": 186.4742, | |
| "train_samples_per_second": 44.36, | |
| "train_steps_per_second": 2.789 | |
| } |