Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use Soulaimen/swin-tiny-patch4-window7-224-shortSleeveCleanedData with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Soulaimen/swin-tiny-patch4-window7-224-shortSleeveCleanedData with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Soulaimen/swin-tiny-patch4-window7-224-shortSleeveCleanedData") 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("Soulaimen/swin-tiny-patch4-window7-224-shortSleeveCleanedData") model = AutoModelForImageClassification.from_pretrained("Soulaimen/swin-tiny-patch4-window7-224-shortSleeveCleanedData") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 20.0, | |
| "eval_accuracy": 0.994535519125683, | |
| "eval_loss": 0.035519640892744064, | |
| "eval_runtime": 30.2809, | |
| "eval_samples_per_second": 30.217, | |
| "eval_steps_per_second": 3.798 | |
| } |