uoft-cs/cifar100
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How to use MazenAmria/swin-base-finetuned-cifar100 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="MazenAmria/swin-base-finetuned-cifar100")
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("MazenAmria/swin-base-finetuned-cifar100")
model = AutoModelForImageClassification.from_pretrained("MazenAmria/swin-base-finetuned-cifar100")This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the cifar100 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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.3536 | 1.0 | 781 | 0.9052 | 0.3141 |
| 0.3254 | 2.0 | 1562 | 0.9117 | 0.2991 |
| 0.0936 | 3.0 | 2343 | 0.9138 | 0.3322 |
| 0.1054 | 4.0 | 3124 | 0.9158 | 0.3483 |
| 0.0269 | 5.0 | 3905 | 0.9201 | 0.3670 |