Instructions to use Prot10/vit-base-patch16-224-for-pre_evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prot10/vit-base-patch16-224-for-pre_evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Prot10/vit-base-patch16-224-for-pre_evaluation") 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("Prot10/vit-base-patch16-224-for-pre_evaluation") model = AutoModelForImageClassification.from_pretrained("Prot10/vit-base-patch16-224-for-pre_evaluation") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 29.54, | |
| "eval_accuracy": 0.39285714285714285, | |
| "eval_loss": 1.604812502861023, | |
| "eval_runtime": 5.7371, | |
| "eval_samples_per_second": 63.447, | |
| "eval_steps_per_second": 2.092, | |
| "total_flos": 4.691568687003814e+18, | |
| "train_loss": 0.9943243801593781, | |
| "train_runtime": 2664.7299, | |
| "train_samples_per_second": 23.068, | |
| "train_steps_per_second": 0.18 | |
| } |