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vit-base-brain-mri-dementia-detection

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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base-brain-mri-dementia-detection
    results: []

vit-base-brain-mri-dementia-detection

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1089
  • Accuracy: 0.9789

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: 0.0002
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.8826 0.3125 100 0.9027 0.575
0.8908 0.625 200 0.8484 0.5984
0.8229 0.9375 300 0.7514 0.6695
0.5299 1.25 400 0.6798 0.7164
0.5207 1.5625 500 0.6466 0.7375
0.4967 1.875 600 0.6303 0.7461
0.3977 2.1875 700 0.7240 0.7719
0.2744 2.5 800 0.3544 0.8734
0.4271 2.8125 900 0.3037 0.8938
0.2484 3.125 1000 0.4111 0.8602
0.0797 3.4375 1100 0.3782 0.8953
0.0662 3.75 1200 0.3096 0.9172
0.0894 4.0625 1300 0.2818 0.9289
0.1005 4.375 1400 0.2164 0.9469
0.0997 4.6875 1500 0.3378 0.9109
0.0715 5.0 1600 0.3627 0.9133
0.0567 5.3125 1700 0.3061 0.9234
0.0558 5.625 1800 0.2393 0.9461
0.0061 5.9375 1900 0.1738 0.9586
0.0449 6.25 2000 0.2094 0.9492
0.0073 6.5625 2100 0.1834 0.9539
0.0425 6.875 2200 0.2847 0.9266
0.0397 7.1875 2300 0.4031 0.9125
0.0284 7.5 2400 0.2995 0.9406
0.0158 7.8125 2500 0.1909 0.9664
0.006 8.125 2600 0.3524 0.9297
0.0017 8.4375 2700 0.1908 0.9617
0.0026 8.75 2800 0.1787 0.9625
0.001 9.0625 2900 0.1329 0.9688
0.0497 9.375 3000 0.1878 0.9594
0.09 9.6875 3100 0.1754 0.9648
0.0046 10.0 3200 0.1584 0.9672
0.0006 10.3125 3300 0.2008 0.9648
0.0008 10.625 3400 0.1272 0.975
0.028 10.9375 3500 0.1453 0.9766
0.0005 11.25 3600 0.1256 0.975
0.0005 11.5625 3700 0.1089 0.9789
0.0004 11.875 3800 0.1098 0.9781
0.0003 12.1875 3900 0.1779 0.9625
0.0163 12.5 4000 0.2500 0.9539
0.0003 12.8125 4100 0.1556 0.9734
0.0003 13.125 4200 0.1205 0.9742
0.0002 13.4375 4300 0.1543 0.9719
0.0002 13.75 4400 0.1548 0.975
0.0003 14.0625 4500 0.1497 0.975
0.0002 14.375 4600 0.2317 0.9641
0.0003 14.6875 4700 0.1418 0.9781
0.0002 15.0 4800 0.1537 0.9734
0.0002 15.3125 4900 0.1426 0.9781
0.0002 15.625 5000 0.1253 0.9820
0.0002 15.9375 5100 0.1128 0.9836
0.0002 16.25 5200 0.1246 0.9805
0.0002 16.5625 5300 0.1137 0.9828
0.0001 16.875 5400 0.1101 0.9844
0.0001 17.1875 5500 0.1112 0.9844
0.0001 17.5 5600 0.1121 0.9844
0.0001 17.8125 5700 0.1129 0.9836
0.0001 18.125 5800 0.1135 0.9844
0.0001 18.4375 5900 0.1140 0.9844
0.0001 18.75 6000 0.1146 0.9844
0.0001 19.0625 6100 0.1150 0.9844
0.0001 19.375 6200 0.1153 0.9844
0.0001 19.6875 6300 0.1155 0.9844
0.0001 20.0 6400 0.1155 0.9844

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1