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swin-tiny-patch4-window7-224-MM_Classification

Image Classification
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swin-tiny-patch4-window7-224-MM_Classification

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

  • Loss: 0.3468
  • Accuracy: 0.8694

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: 128
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 512
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0476 1.0 19 0.7707 0.6530
0.6226 2.0 38 0.4743 0.8105
0.4477 3.0 57 0.4133 0.8323
0.3963 4.0 76 0.3813 0.8476
0.3694 5.0 95 0.3753 0.8540
0.3451 6.0 114 0.3587 0.8489
0.3382 7.0 133 0.3531 0.8451
0.3253 8.0 152 0.3498 0.8579
0.3121 9.0 171 0.3437 0.8579
0.2855 10.0 190 0.3447 0.8656
0.2961 11.0 209 0.3350 0.8617
0.273 12.0 228 0.3484 0.8566
0.2745 13.0 247 0.3433 0.8604
0.2613 14.0 266 0.3498 0.8643
0.2527 15.0 285 0.3365 0.8579
0.2619 16.0 304 0.3450 0.8617
0.2436 17.0 323 0.3454 0.8681
0.2518 18.0 342 0.3437 0.8681
0.243 19.0 361 0.3468 0.8694
0.2415 20.0 380 0.3455 0.8694

Framework versions

  • Transformers 4.43.3
  • Pytorch 1.13.1+cu117
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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Image Classification
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microsoft/swin-tiny-patch4-window7-224
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Evaluation results

View on Papers With Code