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smids_3x_deit_small_adamax_00001_fold2

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_adamax_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: 0.8735440931780366

smids_3x_deit_small_adamax_00001_fold2

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

  • Loss: 0.9125
  • Accuracy: 0.8735

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
0.3392 1.0 225 0.3664 0.8502
0.2605 2.0 450 0.3323 0.8719
0.212 3.0 675 0.3215 0.8686
0.2229 4.0 900 0.3309 0.8652
0.106 5.0 1125 0.3345 0.8802
0.0845 6.0 1350 0.3616 0.8719
0.0626 7.0 1575 0.3907 0.8686
0.0326 8.0 1800 0.4483 0.8669
0.0372 9.0 2025 0.4833 0.8652
0.0087 10.0 2250 0.5521 0.8735
0.0217 11.0 2475 0.5679 0.8752
0.0111 12.0 2700 0.6269 0.8702
0.011 13.0 2925 0.6480 0.8702
0.0061 14.0 3150 0.6728 0.8686
0.0004 15.0 3375 0.7336 0.8669
0.0093 16.0 3600 0.7662 0.8702
0.0044 17.0 3825 0.7704 0.8752
0.0001 18.0 4050 0.7907 0.8735
0.0005 19.0 4275 0.7929 0.8669
0.0001 20.0 4500 0.8179 0.8669
0.0001 21.0 4725 0.8135 0.8785
0.0001 22.0 4950 0.8581 0.8702
0.0037 23.0 5175 0.8366 0.8719
0.0001 24.0 5400 0.8672 0.8686
0.0168 25.0 5625 0.8621 0.8686
0.0001 26.0 5850 0.8633 0.8702
0.0 27.0 6075 0.8679 0.8669
0.0001 28.0 6300 0.8863 0.8735
0.0001 29.0 6525 0.8794 0.8686
0.0145 30.0 6750 0.8923 0.8686
0.0 31.0 6975 0.8806 0.8719
0.0 32.0 7200 0.8844 0.8686
0.0001 33.0 7425 0.8917 0.8669
0.0 34.0 7650 0.8891 0.8719
0.0 35.0 7875 0.8984 0.8735
0.0077 36.0 8100 0.8879 0.8752
0.0 37.0 8325 0.9058 0.8702
0.0 38.0 8550 0.9002 0.8686
0.0096 39.0 8775 0.9018 0.8752
0.0 40.0 9000 0.9051 0.8752
0.0 41.0 9225 0.9023 0.8702
0.0 42.0 9450 0.9103 0.8752
0.0 43.0 9675 0.9151 0.8735
0.0 44.0 9900 0.9097 0.8735
0.0 45.0 10125 0.9063 0.8702
0.0 46.0 10350 0.9129 0.8735
0.0 47.0 10575 0.9170 0.8735
0.0 48.0 10800 0.9138 0.8735
0.0048 49.0 11025 0.9128 0.8735
0.0048 50.0 11250 0.9125 0.8735

Framework versions

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