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BilalMuftuoglu
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deit-base-distilled-patch16-224-85-fold2

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deit-base-distilled-patch16-224-85-fold2

This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1991
  • Accuracy: 0.9318

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
No log 1.0 2 1.2745 0.2727
No log 2.0 4 0.8028 0.4091
No log 3.0 6 0.7456 0.7045
No log 4.0 8 0.7982 0.7045
0.7325 5.0 10 0.6233 0.7045
0.7325 6.0 12 0.5093 0.7273
0.7325 7.0 14 0.5566 0.7045
0.7325 8.0 16 0.6839 0.7045
0.7325 9.0 18 0.4821 0.75
0.4472 10.0 20 0.4365 0.7727
0.4472 11.0 22 0.5158 0.7273
0.4472 12.0 24 0.4196 0.8182
0.4472 13.0 26 0.3599 0.8409
0.4472 14.0 28 0.3604 0.8636
0.3483 15.0 30 0.3634 0.8182
0.3483 16.0 32 0.2803 0.8864
0.3483 17.0 34 0.2592 0.8864
0.3483 18.0 36 0.2655 0.9091
0.3483 19.0 38 0.2333 0.8864
0.2514 20.0 40 0.2590 0.8636
0.2514 21.0 42 0.2642 0.8864
0.2514 22.0 44 0.2637 0.8864
0.2514 23.0 46 0.1991 0.9318
0.2514 24.0 48 0.1941 0.9091
0.1847 25.0 50 0.1868 0.8864
0.1847 26.0 52 0.1828 0.8864
0.1847 27.0 54 0.1711 0.8864
0.1847 28.0 56 0.2423 0.8864
0.1847 29.0 58 0.2162 0.8864
0.1501 30.0 60 0.1854 0.9091
0.1501 31.0 62 0.3071 0.8636
0.1501 32.0 64 0.2435 0.8864
0.1501 33.0 66 0.1728 0.9091
0.1501 34.0 68 0.1644 0.9091
0.13 35.0 70 0.2768 0.8409
0.13 36.0 72 0.1539 0.9318
0.13 37.0 74 0.2580 0.9091
0.13 38.0 76 0.1783 0.8864
0.13 39.0 78 0.1782 0.8636
0.1357 40.0 80 0.2035 0.8864
0.1357 41.0 82 0.2117 0.8864
0.1357 42.0 84 0.1793 0.9091
0.1357 43.0 86 0.2002 0.9091
0.1357 44.0 88 0.2366 0.8864
0.105 45.0 90 0.2008 0.9318
0.105 46.0 92 0.2368 0.8864
0.105 47.0 94 0.2142 0.8864
0.105 48.0 96 0.2117 0.8864
0.105 49.0 98 0.2621 0.8864
0.1091 50.0 100 0.2231 0.8864
0.1091 51.0 102 0.1946 0.9318
0.1091 52.0 104 0.2001 0.9318
0.1091 53.0 106 0.2031 0.9091
0.1091 54.0 108 0.2078 0.9091
0.1054 55.0 110 0.2250 0.9091
0.1054 56.0 112 0.2180 0.9091
0.1054 57.0 114 0.1915 0.9318
0.1054 58.0 116 0.2227 0.8864
0.1054 59.0 118 0.2352 0.8864
0.0982 60.0 120 0.2329 0.8864
0.0982 61.0 122 0.2135 0.9091
0.0982 62.0 124 0.1949 0.8864
0.0982 63.0 126 0.2149 0.9318
0.0982 64.0 128 0.2435 0.9091
0.0808 65.0 130 0.2541 0.9091
0.0808 66.0 132 0.2447 0.9091
0.0808 67.0 134 0.1904 0.9318
0.0808 68.0 136 0.2437 0.9091
0.0808 69.0 138 0.3593 0.8864
0.0843 70.0 140 0.4187 0.8864
0.0843 71.0 142 0.3510 0.8864
0.0843 72.0 144 0.2315 0.9091
0.0843 73.0 146 0.2049 0.9091
0.0843 74.0 148 0.2150 0.9091
0.0942 75.0 150 0.2116 0.9091
0.0942 76.0 152 0.2014 0.9091
0.0942 77.0 154 0.2198 0.9091
0.0942 78.0 156 0.2538 0.9318
0.0942 79.0 158 0.2755 0.9318
0.0884 80.0 160 0.2491 0.9091
0.0884 81.0 162 0.2100 0.9091
0.0884 82.0 164 0.1977 0.9091
0.0884 83.0 166 0.1979 0.9091
0.0884 84.0 168 0.2145 0.9091
0.0637 85.0 170 0.2192 0.9091
0.0637 86.0 172 0.2055 0.9318
0.0637 87.0 174 0.1994 0.9318
0.0637 88.0 176 0.1975 0.9091
0.0637 89.0 178 0.1974 0.9091
0.0923 90.0 180 0.1965 0.9091
0.0923 91.0 182 0.1925 0.9091
0.0923 92.0 184 0.1942 0.9091
0.0923 93.0 186 0.1969 0.9318
0.0923 94.0 188 0.1949 0.9318
0.0657 95.0 190 0.1904 0.9318
0.0657 96.0 192 0.1877 0.9091
0.0657 97.0 194 0.1885 0.9091
0.0657 98.0 196 0.1902 0.9318
0.0657 99.0 198 0.1922 0.9318
0.0822 100.0 200 0.1932 0.9318

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1
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Evaluation results

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