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hushem_1x_deit_tiny_adamax_lr001_fold3

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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_lr001_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: 0.5813953488372093

hushem_1x_deit_tiny_adamax_lr001_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: 1.6696
  • Accuracy: 0.5814

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
  • 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 4.2125 0.2558
No log 2.0 3 1.4682 0.2558
No log 2.67 4 1.6910 0.2558
No log 4.0 6 1.4476 0.2558
No log 4.67 7 1.3895 0.2558
No log 6.0 9 1.3751 0.2558
1.9073 6.67 10 1.3741 0.3953
1.9073 8.0 12 1.3957 0.3488
1.9073 8.67 13 1.3369 0.4419
1.9073 10.0 15 1.2847 0.4186
1.9073 10.67 16 1.3400 0.3953
1.9073 12.0 18 1.2676 0.3953
1.9073 12.67 19 1.2806 0.3721
1.1656 14.0 21 1.3652 0.3023
1.1656 14.67 22 1.3370 0.4419
1.1656 16.0 24 1.5165 0.3721
1.1656 16.67 25 1.5828 0.3953
1.1656 18.0 27 1.3210 0.3953
1.1656 18.67 28 1.3473 0.4419
0.9249 20.0 30 1.4346 0.4651
0.9249 20.67 31 1.3840 0.3953
0.9249 22.0 33 1.3578 0.4884
0.9249 22.67 34 1.3339 0.4884
0.9249 24.0 36 1.3509 0.4884
0.9249 24.67 37 1.3931 0.4884
0.9249 26.0 39 1.5691 0.5116
0.5495 26.67 40 1.5953 0.5349
0.5495 28.0 42 1.6688 0.5814
0.5495 28.67 43 1.6795 0.5581
0.5495 30.0 45 1.6839 0.5814
0.5495 30.67 46 1.6666 0.5814
0.5495 32.0 48 1.6555 0.5814
0.5495 32.67 49 1.6646 0.5814
0.2333 33.33 50 1.6696 0.5814

Framework versions

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