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hushem_1x_deit_tiny_adamax_lr00001_fold3

Image Classification
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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_1x_deit_tiny_adamax_lr00001_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: 0.46511627906976744

hushem_1x_deit_tiny_adamax_lr00001_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: 1.0802
  • Accuracy: 0.4651

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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 0.67 1 1.5307 0.2093
No log 2.0 3 1.3769 0.3023
No log 2.67 4 1.3327 0.3721
No log 4.0 6 1.2794 0.4419
No log 4.67 7 1.2620 0.4419
No log 6.0 9 1.2352 0.4884
1.4092 6.67 10 1.2244 0.4884
1.4092 8.0 12 1.2093 0.4884
1.4092 8.67 13 1.2029 0.4884
1.4092 10.0 15 1.1956 0.4651
1.4092 10.67 16 1.1914 0.4651
1.4092 12.0 18 1.1838 0.4651
1.4092 12.67 19 1.1805 0.4651
1.1598 14.0 21 1.1690 0.4419
1.1598 14.67 22 1.1624 0.4419
1.1598 16.0 24 1.1483 0.4186
1.1598 16.67 25 1.1431 0.4186
1.1598 18.0 27 1.1284 0.4186
1.1598 18.67 28 1.1216 0.4419
0.9892 20.0 30 1.1096 0.4419
0.9892 20.67 31 1.1035 0.4651
0.9892 22.0 33 1.0952 0.4651
0.9892 22.67 34 1.0922 0.4651
0.9892 24.0 36 1.0880 0.4651
0.9892 24.67 37 1.0863 0.4651
0.9892 26.0 39 1.0835 0.4651
0.8902 26.67 40 1.0825 0.4651
0.8902 28.0 42 1.0818 0.4651
0.8902 28.67 43 1.0817 0.4651
0.8902 30.0 45 1.0810 0.4651
0.8902 30.67 46 1.0810 0.4651
0.8902 32.0 48 1.0805 0.4651
0.8902 32.67 49 1.0803 0.4651
0.8497 33.33 50 1.0802 0.4651

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1