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smids_5x_deit_tiny_rms_001_fold3

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_tiny_rms_001_fold3
    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: 0.7766666666666666

smids_5x_deit_tiny_rms_001_fold3

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

  • Loss: 0.8753
  • Accuracy: 0.7767

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
0.7906 1.0 375 0.9128 0.4983
0.7765 2.0 750 0.9232 0.4617
0.7977 3.0 1125 0.8743 0.5267
0.8093 4.0 1500 0.7926 0.5767
0.8508 5.0 1875 0.7894 0.5733
0.7532 6.0 2250 0.7991 0.6117
0.7584 7.0 2625 0.7566 0.625
0.7398 8.0 3000 0.7364 0.6083
0.7009 9.0 3375 0.7452 0.64
0.7014 10.0 3750 0.7192 0.6433
0.7226 11.0 4125 0.7119 0.6383
0.7293 12.0 4500 0.7180 0.6467
0.6344 13.0 4875 0.7612 0.6117
0.6251 14.0 5250 0.7810 0.66
0.6301 15.0 5625 0.6950 0.6733
0.6252 16.0 6000 0.7106 0.6767
0.688 17.0 6375 0.7082 0.6883
0.7261 18.0 6750 0.6859 0.6883
0.5633 19.0 7125 0.6734 0.7033
0.6092 20.0 7500 0.6580 0.7283
0.4728 21.0 7875 0.6793 0.7033
0.5681 22.0 8250 0.6598 0.7217
0.5951 23.0 8625 0.6134 0.7533
0.6592 24.0 9000 0.5954 0.7467
0.5215 25.0 9375 0.5847 0.74
0.5272 26.0 9750 0.6243 0.7017
0.5866 27.0 10125 0.6339 0.7233
0.5766 28.0 10500 0.5466 0.765
0.463 29.0 10875 0.5734 0.7583
0.5041 30.0 11250 0.5320 0.775
0.5133 31.0 11625 0.5507 0.7683
0.5402 32.0 12000 0.5711 0.7517
0.4526 33.0 12375 0.5736 0.7483
0.4724 34.0 12750 0.5009 0.79
0.3951 35.0 13125 0.5483 0.77
0.3876 36.0 13500 0.5689 0.755
0.3627 37.0 13875 0.5639 0.7733
0.4378 38.0 14250 0.5663 0.765
0.3725 39.0 14625 0.5574 0.7867
0.3444 40.0 15000 0.5740 0.7733
0.3158 41.0 15375 0.5671 0.7717
0.29 42.0 15750 0.6455 0.78
0.3784 43.0 16125 0.6093 0.785
0.318 44.0 16500 0.6835 0.7683
0.2949 45.0 16875 0.7092 0.7733
0.2996 46.0 17250 0.6699 0.7767
0.2938 47.0 17625 0.7545 0.7917
0.2248 48.0 18000 0.8050 0.775
0.2309 49.0 18375 0.8518 0.7767
0.1878 50.0 18750 0.8753 0.7767

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.1+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2