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smids_3x_deit_tiny_sgd_00001_fold4

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_sgd_00001_fold4
    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: 0.39166666666666666

smids_3x_deit_tiny_sgd_00001_fold4

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: 1.1127
  • Accuracy: 0.3917

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
1.3668 1.0 225 1.3432 0.3467
1.2646 2.0 450 1.3216 0.3467
1.3414 3.0 675 1.3018 0.3417
1.249 4.0 900 1.2839 0.3417
1.3057 5.0 1125 1.2676 0.345
1.2751 6.0 1350 1.2530 0.35
1.2676 7.0 1575 1.2399 0.3483
1.1849 8.0 1800 1.2280 0.355
1.2324 9.0 2025 1.2173 0.365
1.2387 10.0 2250 1.2077 0.365
1.1505 11.0 2475 1.1990 0.3767
1.2698 12.0 2700 1.1911 0.37
1.1755 13.0 2925 1.1841 0.3717
1.1872 14.0 3150 1.1777 0.3717
1.2051 15.0 3375 1.1720 0.3683
1.1902 16.0 3600 1.1669 0.3733
1.144 17.0 3825 1.1622 0.38
1.0991 18.0 4050 1.1579 0.375
1.1454 19.0 4275 1.1539 0.38
1.1739 20.0 4500 1.1503 0.3717
1.193 21.0 4725 1.1470 0.375
1.154 22.0 4950 1.1439 0.3783
1.1465 23.0 5175 1.1410 0.3733
1.1397 24.0 5400 1.1384 0.3733
1.1433 25.0 5625 1.1360 0.375
1.1492 26.0 5850 1.1337 0.3717
1.1616 27.0 6075 1.1316 0.37
1.137 28.0 6300 1.1297 0.3767
1.1705 29.0 6525 1.1279 0.3817
1.1514 30.0 6750 1.1262 0.3817
1.1348 31.0 6975 1.1247 0.385
1.152 32.0 7200 1.1232 0.3817
1.1108 33.0 7425 1.1219 0.385
1.1032 34.0 7650 1.1207 0.3867
1.0973 35.0 7875 1.1195 0.3833
1.1492 36.0 8100 1.1185 0.3867
1.1046 37.0 8325 1.1176 0.3883
1.1281 38.0 8550 1.1167 0.3883
1.0952 39.0 8775 1.1160 0.3917
1.0675 40.0 9000 1.1153 0.3883
1.1354 41.0 9225 1.1147 0.39
1.1352 42.0 9450 1.1142 0.3883
1.1158 43.0 9675 1.1138 0.39
1.1146 44.0 9900 1.1135 0.39
1.0831 45.0 10125 1.1132 0.39
1.1243 46.0 10350 1.1130 0.39
1.0703 47.0 10575 1.1128 0.3917
1.1356 48.0 10800 1.1127 0.3917
1.1249 49.0 11025 1.1127 0.3917
1.0942 50.0 11250 1.1127 0.3917

Framework versions

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