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smids_3x_deit_base_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-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_base_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: 0.8818635607321131

smids_3x_deit_base_adamax_00001_fold2

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: 0.7483
  • Accuracy: 0.8819

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
0.2908 1.0 225 0.3519 0.8469
0.2412 2.0 450 0.3425 0.8652
0.1561 3.0 675 0.3113 0.8752
0.1722 4.0 900 0.3333 0.8819
0.0793 5.0 1125 0.3397 0.8869
0.0512 6.0 1350 0.3703 0.8902
0.0408 7.0 1575 0.3948 0.8869
0.0153 8.0 1800 0.4474 0.8852
0.0036 9.0 2025 0.5055 0.8819
0.0009 10.0 2250 0.5138 0.8918
0.0011 11.0 2475 0.5776 0.8752
0.0004 12.0 2700 0.6002 0.8785
0.0004 13.0 2925 0.6053 0.8819
0.0002 14.0 3150 0.6097 0.8918
0.0002 15.0 3375 0.6366 0.8819
0.0001 16.0 3600 0.6507 0.8819
0.0042 17.0 3825 0.6732 0.8869
0.0001 18.0 4050 0.6626 0.8852
0.0001 19.0 4275 0.6800 0.8885
0.0001 20.0 4500 0.6886 0.8852
0.0001 21.0 4725 0.7001 0.8819
0.0001 22.0 4950 0.7256 0.8869
0.008 23.0 5175 0.7472 0.8918
0.0001 24.0 5400 0.7160 0.8835
0.0075 25.0 5625 0.7354 0.8852
0.0001 26.0 5850 0.7213 0.8819
0.0001 27.0 6075 0.7101 0.8835
0.0 28.0 6300 0.7245 0.8819
0.0001 29.0 6525 0.7475 0.8869
0.0058 30.0 6750 0.7235 0.8852
0.0 31.0 6975 0.7151 0.8852
0.0 32.0 7200 0.7303 0.8852
0.0 33.0 7425 0.7353 0.8835
0.0 34.0 7650 0.7337 0.8835
0.0 35.0 7875 0.7550 0.8835
0.0034 36.0 8100 0.7409 0.8885
0.0 37.0 8325 0.7323 0.8769
0.0 38.0 8550 0.7381 0.8835
0.0026 39.0 8775 0.7392 0.8819
0.0 40.0 9000 0.7428 0.8819
0.0 41.0 9225 0.7496 0.8835
0.0 42.0 9450 0.7433 0.8835
0.0 43.0 9675 0.7393 0.8852
0.0 44.0 9900 0.7435 0.8835
0.0 45.0 10125 0.7479 0.8835
0.0 46.0 10350 0.7454 0.8835
0.0 47.0 10575 0.7458 0.8802
0.0 48.0 10800 0.7464 0.8819
0.0037 49.0 11025 0.7477 0.8835
0.0037 50.0 11250 0.7483 0.8819

Framework versions

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