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cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epochs

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: 1.0025
  • Accuracy: 0.5947

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
1.6956 1.0 1252 1.4843 0.3970
1.5633 2.0 2504 1.2584 0.4782
1.5568 3.0 3756 1.1976 0.4918
1.4727 4.0 5009 1.1884 0.4916
1.468 5.0 6261 1.1909 0.4889
1.4663 6.0 7513 1.1263 0.5288
1.4409 7.0 8765 1.0967 0.5441
1.4329 8.0 10018 1.0976 0.5388
1.4842 9.0 11270 1.1076 0.5315
1.4253 10.0 12522 1.0634 0.5511
1.3888 11.0 13774 1.0489 0.5634
1.3681 12.0 15027 1.0663 0.5567
1.3802 13.0 16279 1.0304 0.5667
1.4016 14.0 17531 1.0592 0.5518
1.376 15.0 18783 1.0080 0.5776
1.3539 16.0 20036 1.0103 0.5742
1.3725 17.0 21288 1.0261 0.5636
1.3104 18.0 22540 1.0304 0.5686
1.3448 19.0 23792 1.0184 0.5687
1.3479 20.0 25045 0.9968 0.5809
1.3517 21.0 26297 1.1350 0.5182
1.3367 22.0 27549 0.9835 0.5867
1.3002 23.0 28801 1.0193 0.5736
1.3238 24.0 30054 0.9820 0.5875
1.2865 25.0 31306 1.0267 0.5617
1.3029 26.0 32558 1.0086 0.5730
1.3173 27.0 33810 0.9750 0.5924
1.297 28.0 35063 0.9851 0.5848
1.3105 29.0 36315 1.0306 0.5685
1.3477 30.0 37567 0.9977 0.5845
1.2565 31.0 38819 0.9900 0.5851
1.2657 32.0 40072 1.0137 0.5862
1.2911 33.0 41324 0.9947 0.5889
1.2539 34.0 42576 0.9821 0.5914
1.2441 35.0 43828 1.0296 0.5763
1.2176 36.0 45081 1.0350 0.5806
1.25 37.0 46333 1.0195 0.5779
1.2647 38.0 47585 1.0021 0.5903
1.2428 39.0 48837 1.0087 0.5892
1.2364 40.0 50090 1.0025 0.5947
1.2083 41.0 51342 1.0427 0.5862
1.2002 42.0 52594 1.0303 0.5878
1.2071 43.0 53846 1.0190 0.5909
1.1536 44.0 55099 1.0314 0.5920
1.2029 45.0 56351 1.0570 0.5839
1.2249 46.0 57603 1.0508 0.5828
1.1913 47.0 58855 1.0493 0.5853
1.1938 48.0 60108 1.0575 0.5857
1.1724 49.0 61360 1.0700 0.5905
1.1536 50.0 62612 1.0841 0.5853
1.1239 51.0 63864 1.0803 0.5865
1.1743 52.0 65117 1.0864 0.5880
1.1414 53.0 66369 1.1224 0.5819
1.1411 54.0 67621 1.1316 0.5780
1.1029 55.0 68873 1.1070 0.5860
1.1353 56.0 70126 1.1247 0.5847
1.1293 57.0 71378 1.1279 0.5805
1.1335 58.0 72630 1.1482 0.5812
1.1157 59.0 73882 1.1960 0.5674
1.0891 60.0 75135 1.1414 0.5848
1.1299 61.0 76387 1.1658 0.5790
1.0828 62.0 77639 1.1753 0.5806
1.0866 63.0 78891 1.1767 0.5755
1.0721 64.0 80144 1.1861 0.5808
1.0682 65.0 81396 1.2083 0.5749
1.0747 66.0 82648 1.2204 0.5755
1.0902 67.0 83900 1.2175 0.5750
1.0381 68.0 85153 1.2445 0.5738
1.049 69.0 86405 1.2674 0.5707
1.0501 70.0 87657 1.2602 0.5740
1.0117 71.0 88909 1.2549 0.5687
1.0179 72.0 90162 1.3010 0.5690
1.0788 73.0 91414 1.2723 0.5726
1.0234 74.0 92666 1.3162 0.5717
1.0325 75.0 93918 1.3136 0.5692
1.0079 76.0 95171 1.3337 0.5655
1.058 77.0 96423 1.3171 0.5719
0.9968 78.0 97675 1.3470 0.5693
1.0217 79.0 98927 1.3418 0.5733
1.0124 80.0 100180 1.3518 0.5700
0.9823 81.0 101432 1.3646 0.5700
0.9627 82.0 102684 1.3658 0.5686
0.9773 83.0 103936 1.3811 0.5674
0.9855 84.0 105189 1.4082 0.5638
0.9928 85.0 106441 1.3877 0.5612
1.0025 86.0 107693 1.3925 0.5653
0.9583 87.0 108945 1.4313 0.5625
0.977 88.0 110198 1.4153 0.5651
0.9825 89.0 111450 1.4426 0.5619
0.9315 90.0 112702 1.4376 0.5643
0.8916 91.0 113954 1.4630 0.5618
0.9495 92.0 115207 1.4501 0.5627
0.9372 93.0 116459 1.4606 0.5622
0.9284 94.0 117711 1.4725 0.5608
0.9266 95.0 118963 1.4680 0.5607
0.8858 96.0 120216 1.4705 0.5626
0.9025 97.0 121468 1.4818 0.5616
0.902 98.0 122720 1.4871 0.5606
0.8961 99.0 123972 1.4881 0.5612
0.9204 99.98 125200 1.4894 0.5609

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.17.0
  • Tokenizers 0.13.3
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