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squarerun2

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metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: squarerun2
    results: []

squarerun2

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: 1.4284
  • F1 Macro: 0.4676
  • F1 Micro: 0.5606
  • F1 Weighted: 0.5361
  • Precision Macro: 0.4718
  • Precision Micro: 0.5606
  • Precision Weighted: 0.5334
  • Recall Macro: 0.4835
  • Recall Micro: 0.5606
  • Recall Weighted: 0.5606
  • Accuracy: 0.5606

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
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Use OptimizerNames.ADAMW_TORCH 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: 20

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
1.9016 1.0 29 1.8764 0.1011 0.2424 0.1401 0.0721 0.2424 0.1001 0.1761 0.2424 0.2424 0.2424
1.8787 2.0 58 1.8750 0.0485 0.2045 0.0695 0.0292 0.2045 0.0418 0.1429 0.2045 0.2045 0.2045
1.9345 3.0 87 1.8624 0.0485 0.2045 0.0695 0.0292 0.2045 0.0418 0.1429 0.2045 0.2045 0.2045
1.6663 4.0 116 1.7239 0.2230 0.3561 0.2738 0.3173 0.3561 0.3549 0.2725 0.3561 0.3561 0.3561
1.3847 5.0 145 1.4880 0.3420 0.4697 0.4038 0.4521 0.4697 0.4846 0.3893 0.4697 0.4697 0.4697
1.6559 6.0 174 1.4056 0.3479 0.4773 0.4108 0.3865 0.4773 0.4276 0.3870 0.4773 0.4773 0.4773
1.335 7.0 203 1.3768 0.3875 0.5152 0.4527 0.3933 0.5152 0.4447 0.4265 0.5152 0.5152 0.5152
1.2514 8.0 232 1.2345 0.4536 0.5606 0.5207 0.4701 0.5606 0.5257 0.4766 0.5606 0.5606 0.5606
0.6979 9.0 261 1.1501 0.5305 0.6364 0.6097 0.5491 0.6364 0.6127 0.5391 0.6364 0.6364 0.6364
1.0417 10.0 290 1.1654 0.5206 0.6136 0.5900 0.5215 0.6136 0.5935 0.5464 0.6136 0.6136 0.6136
0.7314 11.0 319 1.1566 0.5376 0.6212 0.6109 0.5387 0.6212 0.6154 0.5514 0.6212 0.6212 0.6212
0.7902 12.0 348 1.1624 0.5397 0.6212 0.6140 0.5422 0.6212 0.6209 0.5505 0.6212 0.6212 0.6212
0.7503 13.0 377 1.1359 0.5377 0.6288 0.6126 0.5472 0.6288 0.6143 0.5455 0.6288 0.6288 0.6288
0.586 14.0 406 1.1512 0.5441 0.6288 0.6141 0.5361 0.6288 0.6033 0.5557 0.6288 0.6288 0.6288
0.6869 15.0 435 1.1306 0.5323 0.6288 0.6117 0.5270 0.6288 0.6043 0.5475 0.6288 0.6288 0.6288
0.5498 16.0 464 1.1293 0.5373 0.6288 0.6117 0.5353 0.6288 0.6039 0.5471 0.6288 0.6288 0.6288
0.5037 17.0 493 1.1635 0.5290 0.6212 0.6005 0.5374 0.6212 0.6022 0.5398 0.6212 0.6212 0.6212
0.3624 18.0 522 1.0994 0.5700 0.6591 0.6414 0.5815 0.6591 0.6409 0.5743 0.6591 0.6591 0.6591
0.3387 19.0 551 1.0944 0.5643 0.6515 0.6367 0.5556 0.6515 0.6268 0.5781 0.6515 0.6515 0.6515
0.4052 20.0 580 1.0934 0.5683 0.6591 0.6432 0.5681 0.6591 0.6393 0.5798 0.6591 0.6591 0.6591

Framework versions

  • Transformers 4.48.1
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0