How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-classification", model="MatanBT/swin-tiny-patch4-window7-224-cifar10")
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("MatanBT/swin-tiny-patch4-window7-224-cifar10")
model = AutoModelForImageClassification.from_pretrained("MatanBT/swin-tiny-patch4-window7-224-cifar10")
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swin-tiny-patch4-window7-224-cifar10

This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0995
  • Accuracy: 0.9803

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 128
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 0.1
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.1430 1.2788 500 0.1258 0.9588
0.0699 2.5575 1000 0.1101 0.9676
0.0489 3.8363 1500 0.1001 0.9713
0.0234 5.1151 2000 0.1054 0.9734
0.0136 6.3939 2500 0.0970 0.98
0.0080 7.6726 3000 0.0995 0.9803
0.0059 8.9514 3500 0.1005 0.9799

Framework versions

  • Transformers 5.3.0
  • Pytorch 2.6.0+cu124
  • Datasets 4.8.3
  • Tokenizers 0.22.2
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