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smids_3x_deit_tiny_rms_001_fold1

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-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_rms_001_fold1
    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: 0.8030050083472454

smids_3x_deit_tiny_rms_001_fold1

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: 2.0122
  • Accuracy: 0.8030

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
0.8369 1.0 226 0.8757 0.5359
0.8499 2.0 452 0.8448 0.5776
0.7754 3.0 678 0.9313 0.5142
0.8771 4.0 904 0.7652 0.6194
0.7781 5.0 1130 0.7375 0.6711
0.7866 6.0 1356 0.7599 0.6394
0.664 7.0 1582 0.7260 0.6711
0.6855 8.0 1808 0.8844 0.5376
0.6098 9.0 2034 0.7118 0.6778
0.6338 10.0 2260 0.6856 0.6962
0.5812 11.0 2486 0.6665 0.6962
0.5909 12.0 2712 0.8126 0.6260
0.5272 13.0 2938 0.6279 0.7329
0.5688 14.0 3164 0.7483 0.6494
0.5214 15.0 3390 0.6410 0.7312
0.5581 16.0 3616 0.6042 0.7412
0.4723 17.0 3842 0.6758 0.7145
0.5595 18.0 4068 0.6233 0.7412
0.5549 19.0 4294 0.6152 0.7329
0.5078 20.0 4520 0.6278 0.7195
0.5707 21.0 4746 0.5335 0.7780
0.4944 22.0 4972 0.6366 0.7396
0.5416 23.0 5198 0.5752 0.7663
0.5022 24.0 5424 0.5999 0.7479
0.5615 25.0 5650 0.5710 0.7596
0.5132 26.0 5876 0.5875 0.7730
0.3982 27.0 6102 0.5830 0.7763
0.4012 28.0 6328 0.7036 0.7563
0.37 29.0 6554 0.6641 0.7429
0.4588 30.0 6780 0.6124 0.7613
0.3873 31.0 7006 0.6238 0.7646
0.3153 32.0 7232 0.6857 0.7613
0.3038 33.0 7458 0.7385 0.7730
0.2793 34.0 7684 0.6805 0.7846
0.2405 35.0 7910 0.7592 0.7846
0.2843 36.0 8136 0.8044 0.7746
0.2771 37.0 8362 0.7613 0.7813
0.2263 38.0 8588 0.8328 0.7679
0.1499 39.0 8814 0.9707 0.7696
0.1482 40.0 9040 1.0206 0.7896
0.1303 41.0 9266 1.1237 0.7947
0.0595 42.0 9492 1.3060 0.7763
0.0163 43.0 9718 1.4374 0.7830
0.0383 44.0 9944 1.5230 0.7863
0.0303 45.0 10170 1.5896 0.7947
0.0051 46.0 10396 1.8469 0.7896
0.0006 47.0 10622 1.9434 0.7880
0.0004 48.0 10848 2.0244 0.7947
0.0004 49.0 11074 1.9864 0.7997
0.0002 50.0 11300 2.0122 0.8030

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.1+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2