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End of training
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metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_rms_001_fold4
    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: 0.7533333333333333

smids_3x_deit_small_rms_001_fold4

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1078
  • Accuracy: 0.7533

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
1.1005 1.0 225 0.9456 0.5067
0.8365 2.0 450 0.8727 0.46
0.7918 3.0 675 0.7967 0.555
0.7924 4.0 900 0.7472 0.62
0.8029 5.0 1125 0.7002 0.6433
0.7311 6.0 1350 0.6514 0.685
0.6939 7.0 1575 0.6643 0.7133
0.6634 8.0 1800 0.6358 0.715
0.6019 9.0 2025 0.6144 0.7067
0.6316 10.0 2250 0.6281 0.6967
0.6363 11.0 2475 0.6280 0.7233
0.645 12.0 2700 0.5846 0.7433
0.6675 13.0 2925 0.5877 0.7333
0.5165 14.0 3150 0.5728 0.75
0.52 15.0 3375 0.6706 0.73
0.5715 16.0 3600 0.6345 0.6983
0.4691 17.0 3825 0.6573 0.7167
0.5674 18.0 4050 0.5616 0.7633
0.5252 19.0 4275 0.5645 0.755
0.4602 20.0 4500 0.6017 0.755
0.4817 21.0 4725 0.5934 0.73
0.5616 22.0 4950 0.5845 0.7583
0.4883 23.0 5175 0.6144 0.7483
0.4781 24.0 5400 0.6001 0.7517
0.4229 25.0 5625 0.5953 0.77
0.4499 26.0 5850 0.6184 0.7767
0.4522 27.0 6075 0.6104 0.7433
0.4849 28.0 6300 0.6429 0.745
0.3967 29.0 6525 0.6127 0.7533
0.429 30.0 6750 0.6125 0.775
0.3969 31.0 6975 0.6078 0.7733
0.3935 32.0 7200 0.6333 0.7517
0.3619 33.0 7425 0.6571 0.795
0.4192 34.0 7650 0.6837 0.7833
0.4245 35.0 7875 0.6680 0.76
0.3704 36.0 8100 0.6540 0.78
0.393 37.0 8325 0.6566 0.7867
0.314 38.0 8550 0.7028 0.7783
0.3398 39.0 8775 0.7698 0.7667
0.3529 40.0 9000 0.7537 0.7633
0.3444 41.0 9225 0.8464 0.775
0.2933 42.0 9450 0.7973 0.7717
0.2384 43.0 9675 0.9278 0.765
0.3008 44.0 9900 0.9100 0.7767
0.2711 45.0 10125 0.9061 0.7683
0.3037 46.0 10350 0.9619 0.7783
0.2347 47.0 10575 1.0097 0.765
0.2672 48.0 10800 1.0493 0.75
0.2045 49.0 11025 1.0959 0.76
0.1823 50.0 11250 1.1078 0.7533

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2