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hushem_40x_deit_tiny_sgd_00001_fold1

Image Classification
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PyTorch
vit
Generated from Trainer
Eval Results
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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: hushem_40x_deit_tiny_sgd_00001_fold1
    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: 0.28888888888888886

hushem_40x_deit_tiny_sgd_00001_fold1

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: 1.3702
  • Accuracy: 0.2889

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
1.5833 1.0 215 1.4002 0.1778
1.5381 2.0 430 1.3990 0.2
1.505 3.0 645 1.3978 0.2222
1.446 4.0 860 1.3967 0.2444
1.4742 5.0 1075 1.3956 0.2222
1.3991 6.0 1290 1.3945 0.2222
1.4142 7.0 1505 1.3933 0.2222
1.4895 8.0 1720 1.3923 0.2222
1.4297 9.0 1935 1.3912 0.2222
1.4803 10.0 2150 1.3901 0.2222
1.4253 11.0 2365 1.3890 0.2222
1.4151 12.0 2580 1.3880 0.2222
1.3649 13.0 2795 1.3870 0.2222
1.4058 14.0 3010 1.3860 0.2444
1.3858 15.0 3225 1.3850 0.2444
1.3985 16.0 3440 1.3841 0.2444
1.4078 17.0 3655 1.3832 0.2444
1.3916 18.0 3870 1.3823 0.2444
1.4138 19.0 4085 1.3814 0.2444
1.3697 20.0 4300 1.3807 0.2444
1.3976 21.0 4515 1.3799 0.2444
1.45 22.0 4730 1.3791 0.2444
1.3757 23.0 4945 1.3784 0.2444
1.4088 24.0 5160 1.3777 0.2667
1.3948 25.0 5375 1.3771 0.2667
1.3916 26.0 5590 1.3764 0.2667
1.3383 27.0 5805 1.3759 0.2667
1.3507 28.0 6020 1.3753 0.2889
1.3823 29.0 6235 1.3748 0.2889
1.3489 30.0 6450 1.3743 0.2889
1.3905 31.0 6665 1.3738 0.2889
1.3646 32.0 6880 1.3734 0.2889
1.394 33.0 7095 1.3730 0.2889
1.3256 34.0 7310 1.3726 0.2889
1.342 35.0 7525 1.3723 0.2889
1.3277 36.0 7740 1.3720 0.2889
1.3815 37.0 7955 1.3717 0.2889
1.3516 38.0 8170 1.3714 0.2889
1.3573 39.0 8385 1.3712 0.2889
1.3764 40.0 8600 1.3710 0.2889
1.3508 41.0 8815 1.3708 0.2889
1.4032 42.0 9030 1.3707 0.2889
1.3548 43.0 9245 1.3705 0.2889
1.3623 44.0 9460 1.3704 0.2889
1.3744 45.0 9675 1.3704 0.2889
1.3298 46.0 9890 1.3703 0.2889
1.352 47.0 10105 1.3703 0.2889
1.363 48.0 10320 1.3702 0.2889
1.3844 49.0 10535 1.3702 0.2889
1.3587 50.0 10750 1.3702 0.2889

Framework versions

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