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smids_1x_beit_base_sgd_001_fold4

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

  • Loss: 0.3889
  • Accuracy: 0.8533

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: 0.001
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0889 1.0 75 1.0669 0.4483
0.8655 2.0 150 0.8559 0.645
0.7785 3.0 225 0.7294 0.7017
0.7379 4.0 300 0.6722 0.7167
0.7102 5.0 375 0.6344 0.7367
0.679 6.0 450 0.6029 0.7583
0.6616 7.0 525 0.5757 0.7667
0.6078 8.0 600 0.5519 0.7983
0.6427 9.0 675 0.5357 0.7867
0.5452 10.0 750 0.5237 0.7983
0.5678 11.0 825 0.5091 0.7933
0.5374 12.0 900 0.4984 0.8083
0.5811 13.0 975 0.4866 0.8033
0.5027 14.0 1050 0.4734 0.81
0.4787 15.0 1125 0.4709 0.805
0.4828 16.0 1200 0.4644 0.8117
0.4631 17.0 1275 0.4587 0.81
0.4343 18.0 1350 0.4468 0.82
0.4904 19.0 1425 0.4435 0.82
0.4433 20.0 1500 0.4398 0.8167
0.4461 21.0 1575 0.4318 0.8283
0.3894 22.0 1650 0.4361 0.8217
0.4398 23.0 1725 0.4269 0.8333
0.4433 24.0 1800 0.4213 0.835
0.4253 25.0 1875 0.4182 0.8333
0.4141 26.0 1950 0.4208 0.835
0.4307 27.0 2025 0.4086 0.8433
0.4132 28.0 2100 0.4078 0.8417
0.41 29.0 2175 0.4074 0.8467
0.4398 30.0 2250 0.4048 0.8383
0.3394 31.0 2325 0.4047 0.8383
0.4174 32.0 2400 0.4015 0.8433
0.4379 33.0 2475 0.3992 0.8433
0.3836 34.0 2550 0.3988 0.845
0.3824 35.0 2625 0.3986 0.845
0.3571 36.0 2700 0.3978 0.8433
0.348 37.0 2775 0.3932 0.8483
0.3852 38.0 2850 0.3961 0.8433
0.4099 39.0 2925 0.3932 0.8483
0.4003 40.0 3000 0.3917 0.8533
0.3921 41.0 3075 0.3931 0.8483
0.3545 42.0 3150 0.3904 0.8533
0.3816 43.0 3225 0.3916 0.8467
0.3789 44.0 3300 0.3906 0.8533
0.373 45.0 3375 0.3905 0.85
0.3767 46.0 3450 0.3894 0.8533
0.3814 47.0 3525 0.3896 0.8517
0.3984 48.0 3600 0.3899 0.85
0.3749 49.0 3675 0.3891 0.8533
0.3605 50.0 3750 0.3889 0.8533

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0
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