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square_run_second_vote

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metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: square_run_second_vote
    results: []

square_run_second_vote

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

  • Loss: 1.1557
  • F1 Macro: 0.5777
  • F1 Micro: 0.6667
  • F1 Weighted: 0.6629
  • Precision Macro: 0.5756
  • Precision Micro: 0.6667
  • Precision Weighted: 0.6734
  • Recall Macro: 0.5912
  • Recall Micro: 0.6667
  • Recall Weighted: 0.6667
  • Accuracy: 0.6667

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss F1 Macro F1 Micro F1 Weighted Precision Macro Precision Micro Precision Weighted Recall Macro Recall Micro Recall Weighted Accuracy
1.8754 1.0 58 1.7961 0.1385 0.2803 0.1722 0.1426 0.2803 0.1603 0.2098 0.2803 0.2803 0.2803
2.0246 2.0 116 2.0138 0.2236 0.3106 0.2484 0.2558 0.3106 0.2692 0.2842 0.3106 0.3106 0.3106
1.6189 3.0 174 1.5039 0.2444 0.3864 0.3195 0.2633 0.3864 0.3301 0.2847 0.3864 0.3864 0.3864
1.3445 4.0 232 1.3982 0.3287 0.4394 0.3866 0.3186 0.4394 0.3730 0.3696 0.4394 0.4394 0.4394
1.3387 5.0 290 1.1920 0.4401 0.5758 0.5265 0.4315 0.5758 0.5031 0.4683 0.5758 0.5758 0.5758
1.1664 6.0 348 1.1778 0.4179 0.5076 0.4988 0.5068 0.5076 0.5862 0.4395 0.5076 0.5076 0.5076
1.1622 7.0 406 1.1723 0.4518 0.5379 0.5251 0.4514 0.5379 0.5526 0.4867 0.5379 0.5379 0.5379
0.9827 8.0 464 1.0619 0.5084 0.6212 0.6074 0.5037 0.6212 0.6140 0.5345 0.6212 0.6212 0.6212
1.3416 9.0 522 1.3995 0.3997 0.5 0.4690 0.4218 0.5 0.5024 0.4509 0.5 0.5 0.5
0.758 10.0 580 1.1693 0.5066 0.5985 0.5836 0.5262 0.5985 0.6031 0.5279 0.5985 0.5985 0.5985
0.7758 11.0 638 1.0800 0.5491 0.6515 0.6320 0.5729 0.6515 0.6501 0.5710 0.6515 0.6515 0.6515
0.2319 12.0 696 1.1553 0.5467 0.6742 0.6410 0.5816 0.6742 0.6699 0.5711 0.6742 0.6742 0.6742
0.3528 13.0 754 1.1685 0.5794 0.6894 0.6711 0.5887 0.6894 0.6752 0.5955 0.6894 0.6894 0.6894
0.6238 14.0 812 1.1781 0.5579 0.6439 0.6285 0.5451 0.6439 0.6278 0.5856 0.6439 0.6439 0.6439
0.1869 15.0 870 1.2305 0.5146 0.6061 0.5983 0.5032 0.6061 0.6013 0.5369 0.6061 0.6061 0.6061
0.1015 16.0 928 1.3576 0.5019 0.5909 0.5932 0.5440 0.5909 0.6312 0.4959 0.5909 0.5909 0.5909
0.3809 17.0 986 1.2998 0.5667 0.6591 0.6527 0.5828 0.6591 0.6885 0.5838 0.6591 0.6591 0.6591
0.0887 18.0 1044 1.4154 0.5572 0.6667 0.6489 0.5682 0.6667 0.6518 0.5683 0.6667 0.6667 0.6667
0.1422 19.0 1102 1.3989 0.5609 0.6667 0.6472 0.5672 0.6667 0.6420 0.5695 0.6667 0.6667 0.6667
0.0037 20.0 1160 1.5134 0.5242 0.6212 0.6078 0.5263 0.6212 0.6093 0.5374 0.6212 0.6212 0.6212
0.0602 21.0 1218 1.5349 0.5660 0.6667 0.6544 0.5710 0.6667 0.6503 0.5671 0.6667 0.6667 0.6667
0.0353 22.0 1276 1.4489 0.6137 0.7045 0.6919 0.6146 0.7045 0.6909 0.6242 0.7045 0.7045 0.7045
0.001 23.0 1334 1.4781 0.5715 0.6667 0.6541 0.5657 0.6667 0.6449 0.5805 0.6667 0.6667 0.6667
0.0007 24.0 1392 1.6326 0.5713 0.6591 0.6511 0.5871 0.6591 0.6648 0.5786 0.6591 0.6591 0.6591
0.0084 25.0 1450 1.5856 0.5684 0.6591 0.6569 0.5662 0.6591 0.6672 0.5802 0.6591 0.6591 0.6591
0.0008 26.0 1508 1.5799 0.5826 0.6818 0.6675 0.5849 0.6818 0.6632 0.5884 0.6818 0.6818 0.6818
0.0053 27.0 1566 1.5308 0.5719 0.6667 0.6556 0.5667 0.6667 0.6524 0.5843 0.6667 0.6667 0.6667
0.0004 28.0 1624 1.5639 0.5732 0.6667 0.6617 0.5684 0.6667 0.6673 0.5867 0.6667 0.6667 0.6667
0.0007 29.0 1682 1.5346 0.5835 0.6742 0.6678 0.5786 0.6742 0.6703 0.5965 0.6742 0.6742 0.6742
0.0004 30.0 1740 1.5232 0.5791 0.6742 0.6661 0.5707 0.6742 0.6628 0.5918 0.6742 0.6742 0.6742

Framework versions

  • Transformers 4.48.2
  • Pytorch 2.6.0+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0