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smids_5x_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_5x_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: 0.7562604340567612

smids_5x_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: 0.6720
  • Accuracy: 0.7563

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
0.9519 1.0 376 0.9699 0.4808
0.8617 2.0 752 0.8618 0.5392
0.8149 3.0 1128 0.8048 0.5893
0.8075 4.0 1504 0.7999 0.5609
0.9135 5.0 1880 0.7865 0.6160
0.783 6.0 2256 0.8586 0.5893
0.725 7.0 2632 0.8054 0.6227
0.6972 8.0 3008 0.7248 0.6444
0.72 9.0 3384 0.7167 0.6661
0.7292 10.0 3760 0.7657 0.6795
0.645 11.0 4136 0.6894 0.6861
0.7059 12.0 4512 0.7066 0.6928
0.7086 13.0 4888 0.7125 0.6995
0.6705 14.0 5264 0.6700 0.7078
0.6566 15.0 5640 0.6881 0.6861
0.5734 16.0 6016 0.7052 0.6694
0.5199 17.0 6392 0.7378 0.6628
0.659 18.0 6768 0.6486 0.7112
0.6288 19.0 7144 0.7161 0.6528
0.566 20.0 7520 0.6171 0.7212
0.6474 21.0 7896 0.6184 0.7262
0.5542 22.0 8272 0.6826 0.6861
0.5759 23.0 8648 0.6131 0.7229
0.6266 24.0 9024 0.6647 0.7112
0.6436 25.0 9400 0.6298 0.7078
0.5378 26.0 9776 0.6147 0.7229
0.534 27.0 10152 0.6258 0.7179
0.4794 28.0 10528 0.6515 0.7095
0.5282 29.0 10904 0.6735 0.6912
0.4828 30.0 11280 0.6279 0.7179
0.5597 31.0 11656 0.6003 0.7295
0.5931 32.0 12032 0.6323 0.7362
0.4604 33.0 12408 0.6185 0.7446
0.473 34.0 12784 0.6171 0.7396
0.5357 35.0 13160 0.6139 0.7279
0.5273 36.0 13536 0.6022 0.7379
0.446 37.0 13912 0.6164 0.7362
0.5051 38.0 14288 0.6160 0.7329
0.5127 39.0 14664 0.6147 0.7629
0.5424 40.0 15040 0.5988 0.7579
0.4672 41.0 15416 0.6152 0.7613
0.4259 42.0 15792 0.6298 0.7429
0.4313 43.0 16168 0.6086 0.7462
0.4716 44.0 16544 0.6307 0.7496
0.4303 45.0 16920 0.6176 0.7513
0.3889 46.0 17296 0.6198 0.7479
0.4191 47.0 17672 0.6340 0.7563
0.3752 48.0 18048 0.6420 0.7596
0.3744 49.0 18424 0.6614 0.7529
0.3137 50.0 18800 0.6720 0.7563

Framework versions

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