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cards-blt-swin-tiny-patch4-window7-224-finetuned-v2

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: 1.2162
  • Accuracy: 0.5022

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.4297 1.0 56 1.1976 0.4933
1.4078 1.99 112 1.1964 0.5011
1.417 2.99 168 1.2025 0.4961
1.4163 4.0 225 1.2295 0.4883
1.4318 5.0 281 1.2330 0.495
1.4383 5.99 337 1.2162 0.5022
1.4212 6.99 393 1.2634 0.4717
1.4346 8.0 450 1.3083 0.4689
1.419 9.0 506 1.2719 0.4806
1.4252 9.99 562 1.3048 0.4911
1.4522 10.99 618 1.2708 0.4794
1.3748 12.0 675 1.3720 0.4383
1.3966 13.0 731 1.3095 0.4594
1.4507 13.99 787 1.2430 0.485
1.4033 14.99 843 1.2728 0.4794
1.3972 16.0 900 1.2611 0.4883
1.4136 17.0 956 1.3166 0.45
1.3992 17.99 1012 1.3103 0.4856
1.3614 18.99 1068 1.3302 0.4422
1.3747 20.0 1125 1.2919 0.4856
1.3868 21.0 1181 1.3166 0.4728
1.3399 21.99 1237 1.3200 0.4672
1.3943 22.99 1293 1.2920 0.4811
1.3635 24.0 1350 1.3109 0.4833
1.3724 25.0 1406 1.3100 0.4644
1.3141 25.99 1462 1.3263 0.4978
1.3576 26.99 1518 1.3307 0.4772
1.3022 28.0 1575 1.3409 0.4978
1.2982 29.0 1631 1.3962 0.4583
1.2657 29.99 1687 1.3329 0.4817
1.3152 30.99 1743 1.2973 0.49
1.2924 32.0 1800 1.3159 0.4833
1.214 33.0 1856 1.3955 0.4833
1.2717 33.99 1912 1.4583 0.46
1.2692 34.99 1968 1.3504 0.4939
1.2127 36.0 2025 1.3784 0.4833
1.1956 37.0 2081 1.4184 0.4817
1.2408 37.99 2137 1.3849 0.4944
1.1699 38.99 2193 1.4298 0.4844
1.1727 40.0 2250 1.4331 0.4772
1.1485 41.0 2306 1.4597 0.4672
1.1668 41.99 2362 1.4429 0.4783
1.1881 42.99 2418 1.4555 0.4839
1.1204 44.0 2475 1.4648 0.4783
1.1523 45.0 2531 1.4744 0.4733
1.1206 45.99 2587 1.4792 0.4906
1.1135 46.99 2643 1.5009 0.4678
1.1227 48.0 2700 1.5480 0.4733
1.1017 49.0 2756 1.5907 0.4644
1.1601 49.99 2812 1.5136 0.47
1.1239 50.99 2868 1.5384 0.4789
1.09 52.0 2925 1.5716 0.4711
1.1023 53.0 2981 1.5736 0.4728
1.1038 53.99 3037 1.5919 0.4556
1.058 54.99 3093 1.5534 0.4772
1.0405 56.0 3150 1.5788 0.4717
1.0172 57.0 3206 1.5855 0.4767
1.0036 57.99 3262 1.6425 0.455
1.0124 58.99 3318 1.6039 0.4678
1.0647 60.0 3375 1.5891 0.4572
1.0143 61.0 3431 1.6265 0.4483
1.0051 61.99 3487 1.6208 0.4633
0.9571 62.99 3543 1.6874 0.4483
0.9838 64.0 3600 1.6778 0.4517
0.9995 65.0 3656 1.6248 0.4722
1.0374 65.99 3712 1.6645 0.4667
0.9483 66.99 3768 1.6307 0.4611
0.9825 68.0 3825 1.6662 0.4661
1.0023 69.0 3881 1.6650 0.46
0.9642 69.99 3937 1.6953 0.4494
0.9687 70.99 3993 1.7076 0.4661
0.9542 72.0 4050 1.7012 0.4656
0.9378 73.0 4106 1.7056 0.4533
0.9542 73.99 4162 1.7331 0.4572
0.9035 74.99 4218 1.7459 0.4417
0.9631 76.0 4275 1.7236 0.465
0.8759 77.0 4331 1.7294 0.455
0.9218 77.99 4387 1.7654 0.4578
0.9077 78.99 4443 1.7234 0.4594
0.8924 80.0 4500 1.7256 0.4683
0.9156 81.0 4556 1.7320 0.4678
0.806 81.99 4612 1.7348 0.4661
0.8863 82.99 4668 1.7514 0.4606
0.8698 84.0 4725 1.7484 0.4661
0.8623 85.0 4781 1.7420 0.4778
0.8643 85.99 4837 1.7636 0.4617
0.8914 86.99 4893 1.7552 0.465
0.837 88.0 4950 1.7552 0.4644
0.8217 89.0 5006 1.7532 0.4639
0.8601 89.99 5062 1.7447 0.4683
0.8293 90.99 5118 1.7622 0.4611
0.8301 92.0 5175 1.7616 0.4633
0.7752 93.0 5231 1.7585 0.4722
0.8533 93.99 5287 1.7842 0.4617
0.8156 94.99 5343 1.7837 0.4622
0.8094 96.0 5400 1.7896 0.4583
0.839 97.0 5456 1.7835 0.465
0.839 97.99 5512 1.7883 0.46
0.7763 98.99 5568 1.7838 0.4594
0.8186 99.56 5600 1.7837 0.4606

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.17.0
  • Tokenizers 0.15.2
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