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finetuned-CK

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metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224-pt22k-ft22k
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: finetuned-CK
    results: []

finetuned-CK

This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1159
  • Accuracy: 0.9873

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: 5e-06
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.87 5 2.0523 0.1139
1.9841 1.91 11 1.9751 0.0633
1.9841 2.96 17 1.8640 0.1392
1.8698 4.0 23 1.7348 0.3291
1.8698 4.87 28 1.6262 0.4430
1.6947 5.91 34 1.4895 0.5823
1.6038 6.96 40 1.3500 0.5949
1.6038 8.0 46 1.1857 0.6835
1.4019 8.87 51 1.0636 0.6962
1.4019 9.91 57 0.9472 0.7468
1.2101 10.96 63 0.8263 0.7722
1.2101 12.0 69 0.7643 0.7595
1.077 12.87 74 0.7260 0.7722
0.9735 13.91 80 0.6628 0.8228
0.9735 14.96 86 0.6245 0.8101
0.8696 16.0 92 0.5579 0.8101
0.8696 16.87 97 0.5309 0.8228
0.858 17.91 103 0.5099 0.8354
0.858 18.96 109 0.4997 0.8354
0.7716 20.0 115 0.4679 0.8608
0.699 20.87 120 0.4455 0.8481
0.699 21.91 126 0.4409 0.8608
0.6768 22.96 132 0.4186 0.8481
0.6768 24.0 138 0.3826 0.8734
0.6303 24.87 143 0.3708 0.8861
0.6303 25.91 149 0.3545 0.8608
0.5786 26.96 155 0.3480 0.8987
0.5188 28.0 161 0.3241 0.9241
0.5188 28.87 166 0.3257 0.9114
0.5349 29.91 172 0.2963 0.9241
0.5349 30.96 178 0.2836 0.9367
0.5208 32.0 184 0.2822 0.9620
0.5208 32.87 189 0.2933 0.9494
0.4458 33.91 195 0.2742 0.9620
0.4716 34.96 201 0.2580 0.9620
0.4716 36.0 207 0.2432 0.9620
0.427 36.87 212 0.2333 0.9747
0.427 37.91 218 0.2115 0.9494
0.4052 38.96 224 0.2044 0.9873
0.3899 40.0 230 0.2081 0.9747
0.3899 40.87 235 0.2110 0.9620
0.4098 41.91 241 0.2099 0.9494
0.4098 42.96 247 0.1899 0.9747
0.3691 44.0 253 0.1783 0.9873
0.3691 44.87 258 0.1768 0.9747
0.3878 45.91 264 0.1836 0.9873
0.3582 46.96 270 0.1876 0.9747
0.3582 48.0 276 0.1761 0.9747
0.356 48.87 281 0.1745 0.9620
0.356 49.91 287 0.1761 0.9620
0.3715 50.96 293 0.1716 0.9873
0.3715 52.0 299 0.1694 0.9620
0.3195 52.87 304 0.1660 0.9873
0.3452 53.91 310 0.1584 0.9873
0.3452 54.96 316 0.1579 0.9620
0.3407 56.0 322 0.1431 0.9873
0.3407 56.87 327 0.1423 0.9873
0.3092 57.91 333 0.1434 0.9747
0.3092 58.96 339 0.1391 0.9873
0.3346 60.0 345 0.1362 0.9873
0.3107 60.87 350 0.1341 0.9873
0.3107 61.91 356 0.1368 0.9873
0.2884 62.96 362 0.1393 0.9747
0.2884 64.0 368 0.1347 0.9873
0.3048 64.87 373 0.1334 0.9873
0.3048 65.91 379 0.1342 0.9747
0.3452 66.96 385 0.1310 0.9747
0.2835 68.0 391 0.1308 0.9873
0.2835 68.87 396 0.1304 0.9747
0.3865 69.91 402 0.1264 0.9873
0.3865 70.96 408 0.1247 0.9873
0.2729 72.0 414 0.1239 0.9873
0.2729 72.87 419 0.1249 0.9747
0.2643 73.91 425 0.1189 0.9873
0.3176 74.96 431 0.1171 0.9873
0.3176 76.0 437 0.1174 0.9873
0.3184 76.87 442 0.1184 0.9873
0.3184 77.91 448 0.1160 0.9873
0.2817 78.96 454 0.1147 0.9873
0.2666 80.0 460 0.1133 0.9873
0.2666 80.87 465 0.1139 0.9873
0.2589 81.91 471 0.1142 0.9873
0.2589 82.96 477 0.1150 0.9873
0.275 84.0 483 0.1156 0.9873
0.275 84.87 488 0.1160 0.9873
0.2644 85.91 494 0.1160 0.9873
0.3187 86.96 500 0.1159 0.9873

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0