uoft-cs/cifar10
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How to use jagriti/swin-tiny-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="jagriti/swin-tiny-patch4-window7-224")
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("jagriti/swin-tiny-patch4-window7-224")
model = AutoModelForImageClassification.from_pretrained("jagriti/swin-tiny-patch4-window7-224")This model is a fine-tuned version of jagriti/swin-tiny-patch4-window7-224 on the cifar10 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4991 | 1.0 | 351 | 0.1543 | 0.947 |
| 0.346 | 2.0 | 703 | 0.1023 | 0.964 |
| 0.3329 | 2.99 | 1053 | 0.0907 | 0.9694 |