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square_run_first_vote_full_pic_75

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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_first_vote_full_pic_75
    results: []

square_run_first_vote_full_pic_75

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: 1.7586
  • F1 Macro: 0.4234
  • F1 Micro: 0.5152
  • F1 Weighted: 0.4789
  • Precision Macro: 0.4562
  • Precision Micro: 0.5152
  • Precision Weighted: 0.5061
  • Recall Macro: 0.4488
  • Recall Micro: 0.5152
  • Recall Weighted: 0.5152
  • Accuracy: 0.5152

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
1.9675 1.0 58 1.9322 0.1051 0.1894 0.1204 0.1047 0.1894 0.1191 0.1661 0.1894 0.1894 0.1894
1.8921 2.0 116 1.9534 0.0786 0.1818 0.0855 0.0551 0.1818 0.0597 0.1656 0.1818 0.1818 0.1818
1.9081 3.0 174 1.8370 0.1526 0.2803 0.1976 0.1283 0.2803 0.1642 0.2117 0.2803 0.2803 0.2803
1.5193 4.0 232 1.7240 0.1963 0.3258 0.2445 0.2948 0.3258 0.3289 0.2476 0.3258 0.3258 0.3258
1.7743 5.0 290 1.5478 0.3382 0.4318 0.3920 0.3494 0.4318 0.4204 0.3837 0.4318 0.4318 0.4318
1.9879 6.0 348 1.5070 0.3157 0.4470 0.3865 0.4597 0.4470 0.5200 0.3499 0.4470 0.4470 0.4470
1.9096 7.0 406 1.4281 0.3859 0.4545 0.4410 0.4248 0.4545 0.4763 0.3931 0.4545 0.4545 0.4545
1.4577 8.0 464 1.4558 0.3862 0.4773 0.4381 0.3827 0.4773 0.4425 0.4346 0.4773 0.4773 0.4773
1.9664 9.0 522 1.5863 0.3757 0.4773 0.4227 0.3967 0.4773 0.4530 0.4288 0.4773 0.4773 0.4773
0.7655 10.0 580 1.3785 0.4015 0.5 0.4621 0.5175 0.5 0.5866 0.4427 0.5 0.5 0.5
0.707 11.0 638 1.3441 0.4772 0.5530 0.5356 0.4915 0.5530 0.5453 0.4861 0.5530 0.5530 0.5530
0.782 12.0 696 1.3983 0.4716 0.5530 0.5325 0.4860 0.5530 0.5432 0.4877 0.5530 0.5530 0.5530
0.7316 13.0 754 1.6155 0.4880 0.5530 0.5497 0.5085 0.5530 0.5892 0.5080 0.5530 0.5530 0.5530
1.0819 14.0 812 1.4869 0.4936 0.5379 0.5312 0.5124 0.5379 0.5370 0.4900 0.5379 0.5379 0.5379
0.8757 15.0 870 1.6936 0.4741 0.5303 0.5300 0.4809 0.5303 0.5481 0.4847 0.5303 0.5303 0.5303
0.7228 16.0 928 1.7370 0.4442 0.5227 0.4986 0.4401 0.5227 0.4939 0.4646 0.5227 0.5227 0.5227
0.3016 17.0 986 1.6977 0.5279 0.5682 0.5642 0.6353 0.5682 0.5994 0.5176 0.5682 0.5682 0.5682
0.2097 18.0 1044 1.9026 0.4769 0.5606 0.5414 0.5384 0.5606 0.5783 0.4819 0.5606 0.5606 0.5606
0.0388 19.0 1102 1.8276 0.5259 0.6136 0.5981 0.5252 0.6136 0.5945 0.5382 0.6136 0.6136 0.6136
0.4837 20.0 1160 1.8658 0.5336 0.5985 0.5863 0.5502 0.5985 0.5866 0.5342 0.5985 0.5985 0.5985
0.1531 21.0 1218 2.0415 0.4703 0.5606 0.5384 0.4917 0.5606 0.5489 0.4762 0.5606 0.5606 0.5606
0.0142 22.0 1276 2.0812 0.4969 0.5303 0.5260 0.5067 0.5303 0.5364 0.5008 0.5303 0.5303 0.5303
0.0036 23.0 1334 2.0662 0.5315 0.5758 0.5781 0.5480 0.5758 0.5925 0.5316 0.5758 0.5758 0.5758
0.0065 24.0 1392 2.1023 0.5090 0.5606 0.5516 0.5140 0.5606 0.5550 0.5154 0.5606 0.5606 0.5606
0.1359 25.0 1450 2.0555 0.4994 0.5455 0.5440 0.5018 0.5455 0.5474 0.5021 0.5455 0.5455 0.5455
0.0037 26.0 1508 2.1745 0.5206 0.5758 0.5691 0.5289 0.5758 0.5695 0.5204 0.5758 0.5758 0.5758
0.0391 27.0 1566 2.2087 0.5204 0.5758 0.5676 0.5335 0.5758 0.5745 0.5228 0.5758 0.5758 0.5758
0.0017 28.0 1624 2.1219 0.5178 0.5682 0.5633 0.5218 0.5682 0.5649 0.5212 0.5682 0.5682 0.5682
0.0015 29.0 1682 2.1455 0.5198 0.5682 0.5618 0.5342 0.5682 0.5641 0.5190 0.5682 0.5682 0.5682
0.0015 30.0 1740 2.1308 0.5192 0.5682 0.5617 0.5315 0.5682 0.5621 0.5190 0.5682 0.5682 0.5682

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.3.1
  • Tokenizers 0.21.0