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hushem_1x_deit_tiny_adamax_lr001_fold4

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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_fold4
    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: 0.7619047619047619

hushem_1x_deit_tiny_adamax_lr001_fold4

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: 0.7197
  • Accuracy: 0.7619

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 4.5038 0.2619
No log 2.0 3 1.5021 0.2381
No log 2.67 4 1.6655 0.2619
No log 4.0 6 1.3927 0.2381
No log 4.67 7 1.4664 0.2381
No log 6.0 9 1.4341 0.2381
1.9815 6.67 10 1.3866 0.5238
1.9815 8.0 12 1.4168 0.2381
1.9815 8.67 13 1.3770 0.2381
1.9815 10.0 15 1.3099 0.2619
1.9815 10.67 16 1.3229 0.2381
1.9815 12.0 18 1.2134 0.5
1.9815 12.67 19 1.1451 0.5238
1.3526 14.0 21 1.1341 0.6429
1.3526 14.67 22 0.9936 0.5952
1.3526 16.0 24 0.8768 0.6905
1.3526 16.67 25 0.9003 0.7143
1.3526 18.0 27 0.7438 0.7857
1.3526 18.67 28 0.6744 0.7143
1.0291 20.0 30 0.6946 0.7381
1.0291 20.67 31 0.6723 0.7381
1.0291 22.0 33 0.7030 0.7619
1.0291 22.67 34 0.6565 0.7857
1.0291 24.0 36 0.6394 0.7619
1.0291 24.67 37 0.7519 0.7143
1.0291 26.0 39 0.7489 0.6667
0.712 26.67 40 0.5267 0.8095
0.712 28.0 42 0.6166 0.7619
0.712 28.67 43 0.7873 0.7143
0.712 30.0 45 0.8388 0.7619
0.712 30.67 46 0.7831 0.7381
0.712 32.0 48 0.7151 0.7619
0.712 32.67 49 0.7126 0.7619
0.4557 33.33 50 0.7197 0.7619

Framework versions

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