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smids_3x_deit_small_rms_0001_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_rms_0001_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: 0.8801996672212978

smids_3x_deit_small_rms_0001_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: 1.1115
  • Accuracy: 0.8802

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: 0.0001
  • 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
0.3676 1.0 225 0.3260 0.8652
0.2682 2.0 450 0.4038 0.8369
0.1969 3.0 675 0.3463 0.8569
0.1563 4.0 900 0.3656 0.8869
0.127 5.0 1125 0.4906 0.8885
0.0496 6.0 1350 0.4366 0.8852
0.0874 7.0 1575 0.6811 0.8735
0.0746 8.0 1800 0.4728 0.9002
0.0301 9.0 2025 0.6425 0.8802
0.0064 10.0 2250 0.6457 0.8852
0.021 11.0 2475 0.6671 0.8752
0.0135 12.0 2700 0.6914 0.8852
0.0087 13.0 2925 0.8348 0.8686
0.0257 14.0 3150 0.6378 0.8769
0.0699 15.0 3375 0.7199 0.8885
0.003 16.0 3600 0.7607 0.8869
0.003 17.0 3825 0.7580 0.8819
0.0003 18.0 4050 0.7463 0.8835
0.0005 19.0 4275 0.6721 0.8852
0.0305 20.0 4500 0.7465 0.8785
0.03 21.0 4725 0.8137 0.8752
0.0098 22.0 4950 0.7797 0.8802
0.0223 23.0 5175 0.8830 0.8735
0.0014 24.0 5400 0.9177 0.8752
0.0318 25.0 5625 1.2159 0.8519
0.0263 26.0 5850 0.9640 0.8669
0.0494 27.0 6075 0.9004 0.8702
0.0002 28.0 6300 1.0163 0.8752
0.0354 29.0 6525 1.0067 0.8752
0.0062 30.0 6750 1.0029 0.8785
0.0239 31.0 6975 0.8464 0.8835
0.0305 32.0 7200 0.8764 0.8752
0.0007 33.0 7425 0.8617 0.8769
0.0 34.0 7650 0.9176 0.8785
0.0 35.0 7875 0.9537 0.8885
0.0028 36.0 8100 0.9078 0.8802
0.0 37.0 8325 0.9401 0.8902
0.0066 38.0 8550 0.9351 0.8802
0.0208 39.0 8775 0.9403 0.8869
0.0 40.0 9000 1.0137 0.8852
0.0103 41.0 9225 1.0628 0.8769
0.0 42.0 9450 0.9758 0.8802
0.0 43.0 9675 1.0037 0.8802
0.0 44.0 9900 1.0404 0.8769
0.0 45.0 10125 1.0618 0.8819
0.0 46.0 10350 1.0847 0.8802
0.0 47.0 10575 1.0984 0.8819
0.0 48.0 10800 1.1045 0.8819
0.0023 49.0 11025 1.1110 0.8802
0.0023 50.0 11250 1.1115 0.8802

Framework versions

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