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smids_3x_deit_base_sgd_001_fold3

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-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_base_sgd_001_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: 0.8866666666666667

smids_3x_deit_base_sgd_001_fold3

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

  • Loss: 0.2808
  • Accuracy: 0.8867

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
  • 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
0.957 1.0 225 0.9484 0.6067
0.7319 2.0 450 0.7399 0.7417
0.5675 3.0 675 0.6023 0.8
0.5071 4.0 900 0.5170 0.8183
0.4486 5.0 1125 0.4641 0.8317
0.3985 6.0 1350 0.4275 0.85
0.3723 7.0 1575 0.4010 0.8483
0.3723 8.0 1800 0.3821 0.8533
0.3669 9.0 2025 0.3688 0.8617
0.3526 10.0 2250 0.3586 0.8667
0.3131 11.0 2475 0.3476 0.8733
0.3182 12.0 2700 0.3438 0.8717
0.329 13.0 2925 0.3317 0.875
0.2936 14.0 3150 0.3264 0.8733
0.281 15.0 3375 0.3211 0.875
0.29 16.0 3600 0.3177 0.875
0.3306 17.0 3825 0.3133 0.8767
0.268 18.0 4050 0.3109 0.875
0.2813 19.0 4275 0.3073 0.8817
0.29 20.0 4500 0.3041 0.8833
0.2543 21.0 4725 0.3034 0.8833
0.2485 22.0 4950 0.3036 0.8833
0.2389 23.0 5175 0.2976 0.885
0.2585 24.0 5400 0.2973 0.885
0.2265 25.0 5625 0.2954 0.8867
0.2806 26.0 5850 0.2939 0.8867
0.2023 27.0 6075 0.2926 0.8867
0.2462 28.0 6300 0.2897 0.885
0.2516 29.0 6525 0.2898 0.8867
0.2125 30.0 6750 0.2889 0.8867
0.2359 31.0 6975 0.2881 0.8883
0.2277 32.0 7200 0.2865 0.8867
0.2555 33.0 7425 0.2855 0.8883
0.202 34.0 7650 0.2847 0.8883
0.2329 35.0 7875 0.2859 0.8883
0.262 36.0 8100 0.2851 0.8883
0.2072 37.0 8325 0.2831 0.8867
0.2187 38.0 8550 0.2836 0.8867
0.2311 39.0 8775 0.2822 0.8867
0.2221 40.0 9000 0.2824 0.8867
0.2051 41.0 9225 0.2822 0.8867
0.2267 42.0 9450 0.2815 0.8867
0.2187 43.0 9675 0.2815 0.8867
0.2138 44.0 9900 0.2815 0.8867
0.2129 45.0 10125 0.2813 0.8867
0.2034 46.0 10350 0.2810 0.8867
0.2287 47.0 10575 0.2808 0.8867
0.2051 48.0 10800 0.2807 0.8867
0.256 49.0 11025 0.2807 0.8867
0.2173 50.0 11250 0.2808 0.8867

Framework versions

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