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End of training
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metadata
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_beit_base_adamax_001_fold5
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7317073170731707

hushem_1x_beit_base_adamax_001_fold5

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: 1.2278
  • Accuracy: 0.7317

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
No log 1.0 6 1.4268 0.2439
1.7859 2.0 12 1.3982 0.2439
1.7859 3.0 18 1.3119 0.4878
1.3869 4.0 24 1.2627 0.4146
1.329 5.0 30 1.0564 0.5610
1.329 6.0 36 1.2486 0.2927
1.2971 7.0 42 1.2260 0.3415
1.2971 8.0 48 1.1669 0.5122
1.2043 9.0 54 1.2078 0.4390
1.166 10.0 60 1.1291 0.4390
1.166 11.0 66 1.4793 0.2683
1.2368 12.0 72 1.1712 0.4390
1.2368 13.0 78 1.1600 0.4146
1.0841 14.0 84 1.1286 0.4146
1.1358 15.0 90 1.0309 0.4878
1.1358 16.0 96 1.0536 0.3902
1.0304 17.0 102 0.9535 0.4878
1.0304 18.0 108 1.1738 0.3659
0.9971 19.0 114 0.9220 0.5122
0.9482 20.0 120 1.0234 0.6829
0.9482 21.0 126 1.0465 0.5366
0.9578 22.0 132 1.0713 0.5854
0.9578 23.0 138 1.1190 0.5122
1.0032 24.0 144 1.0303 0.6341
0.9765 25.0 150 0.9143 0.6098
0.9765 26.0 156 0.9675 0.6098
0.8768 27.0 162 0.8561 0.6341
0.8768 28.0 168 1.0406 0.4878
0.813 29.0 174 1.2443 0.6098
0.8566 30.0 180 0.8255 0.6341
0.8566 31.0 186 0.8471 0.6829
0.7675 32.0 192 0.9851 0.6829
0.7675 33.0 198 1.1042 0.6829
0.7167 34.0 204 1.0172 0.6829
0.6799 35.0 210 1.1228 0.5366
0.6799 36.0 216 1.1880 0.7317
0.6558 37.0 222 1.1922 0.7317
0.6558 38.0 228 1.4663 0.6585
0.5997 39.0 234 1.0459 0.7317
0.579 40.0 240 1.1555 0.7073
0.579 41.0 246 1.1889 0.7073
0.5728 42.0 252 1.2278 0.7317
0.5728 43.0 258 1.2278 0.7317
0.5177 44.0 264 1.2278 0.7317
0.5591 45.0 270 1.2278 0.7317
0.5591 46.0 276 1.2278 0.7317
0.5528 47.0 282 1.2278 0.7317
0.5528 48.0 288 1.2278 0.7317
0.575 49.0 294 1.2278 0.7317
0.5528 50.0 300 1.2278 0.7317

Framework versions

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