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hushem_40x_deit_base_sgd_00001_fold2

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
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metadata
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_40x_deit_base_sgd_00001_fold2
    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: 0.3111111111111111

hushem_40x_deit_base_sgd_00001_fold2

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

  • Loss: 1.3898
  • Accuracy: 0.3111

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: 1e-05
  • 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
1.4168 1.0 215 1.4077 0.2444
1.3843 2.0 430 1.4068 0.2444
1.4045 3.0 645 1.4059 0.2444
1.3944 4.0 860 1.4051 0.2444
1.3979 5.0 1075 1.4043 0.2444
1.4212 6.0 1290 1.4036 0.2667
1.4197 7.0 1505 1.4029 0.2667
1.369 8.0 1720 1.4022 0.2667
1.3853 9.0 1935 1.4015 0.2667
1.4053 10.0 2150 1.4008 0.2667
1.3723 11.0 2365 1.4002 0.2667
1.3571 12.0 2580 1.3996 0.2667
1.3936 13.0 2795 1.3990 0.2667
1.3779 14.0 3010 1.3985 0.2667
1.3861 15.0 3225 1.3979 0.2667
1.4005 16.0 3440 1.3974 0.2889
1.3769 17.0 3655 1.3969 0.2889
1.3909 18.0 3870 1.3964 0.2889
1.3834 19.0 4085 1.3960 0.2889
1.3642 20.0 4300 1.3956 0.2889
1.3863 21.0 4515 1.3951 0.2889
1.3863 22.0 4730 1.3947 0.2889
1.3703 23.0 4945 1.3944 0.2889
1.3733 24.0 5160 1.3940 0.2889
1.3751 25.0 5375 1.3937 0.3111
1.3799 26.0 5590 1.3933 0.3111
1.3637 27.0 5805 1.3930 0.3111
1.3658 28.0 6020 1.3927 0.3111
1.3837 29.0 6235 1.3924 0.3111
1.3573 30.0 6450 1.3922 0.3111
1.3483 31.0 6665 1.3919 0.3111
1.3737 32.0 6880 1.3917 0.3111
1.3567 33.0 7095 1.3915 0.3111
1.3764 34.0 7310 1.3913 0.3111
1.3646 35.0 7525 1.3911 0.3111
1.3557 36.0 7740 1.3909 0.3111
1.3829 37.0 7955 1.3907 0.3111
1.3713 38.0 8170 1.3906 0.3111
1.3468 39.0 8385 1.3905 0.3111
1.3527 40.0 8600 1.3903 0.3111
1.3629 41.0 8815 1.3902 0.3111
1.3464 42.0 9030 1.3901 0.3111
1.3709 43.0 9245 1.3901 0.3111
1.3524 44.0 9460 1.3900 0.3111
1.3532 45.0 9675 1.3899 0.3111
1.3657 46.0 9890 1.3899 0.3111
1.3891 47.0 10105 1.3899 0.3111
1.3666 48.0 10320 1.3898 0.3111
1.3713 49.0 10535 1.3898 0.3111
1.3614 50.0 10750 1.3898 0.3111

Framework versions

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