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End of training
97c2eab
metadata
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_5x_deit_base_sgd_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: 0.13333333333333333

hushem_5x_deit_base_sgd_0001_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: 1.4239
  • Accuracy: 0.1333

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
1.43 1.0 27 1.4766 0.1333
1.4102 2.0 54 1.4737 0.1333
1.4007 3.0 81 1.4709 0.1333
1.4122 4.0 108 1.4682 0.1333
1.4031 5.0 135 1.4658 0.1333
1.4094 6.0 162 1.4633 0.1333
1.3941 7.0 189 1.4611 0.1333
1.4105 8.0 216 1.4588 0.1333
1.4006 9.0 243 1.4567 0.1333
1.3895 10.0 270 1.4547 0.1333
1.3922 11.0 297 1.4528 0.1333
1.3661 12.0 324 1.4510 0.1333
1.397 13.0 351 1.4492 0.1333
1.3778 14.0 378 1.4476 0.1333
1.3888 15.0 405 1.4461 0.1333
1.3865 16.0 432 1.4446 0.1333
1.3782 17.0 459 1.4432 0.1333
1.3766 18.0 486 1.4418 0.1333
1.3767 19.0 513 1.4404 0.1333
1.3782 20.0 540 1.4392 0.1333
1.3664 21.0 567 1.4381 0.1333
1.3644 22.0 594 1.4370 0.1333
1.386 23.0 621 1.4359 0.1333
1.3679 24.0 648 1.4349 0.1333
1.3604 25.0 675 1.4339 0.1333
1.3727 26.0 702 1.4330 0.1333
1.3624 27.0 729 1.4321 0.1333
1.3512 28.0 756 1.4313 0.1333
1.3641 29.0 783 1.4305 0.1333
1.3697 30.0 810 1.4298 0.1333
1.3661 31.0 837 1.4292 0.1333
1.3762 32.0 864 1.4286 0.1333
1.3653 33.0 891 1.4280 0.1333
1.3526 34.0 918 1.4274 0.1333
1.3565 35.0 945 1.4269 0.1333
1.3671 36.0 972 1.4265 0.1333
1.3721 37.0 999 1.4261 0.1333
1.3579 38.0 1026 1.4257 0.1333
1.3662 39.0 1053 1.4254 0.1333
1.3491 40.0 1080 1.4250 0.1333
1.3508 41.0 1107 1.4248 0.1333
1.3555 42.0 1134 1.4245 0.1333
1.3427 43.0 1161 1.4244 0.1333
1.3543 44.0 1188 1.4242 0.1333
1.3592 45.0 1215 1.4241 0.1333
1.3632 46.0 1242 1.4240 0.1333
1.3606 47.0 1269 1.4239 0.1333
1.3593 48.0 1296 1.4239 0.1333
1.3726 49.0 1323 1.4239 0.1333
1.3608 50.0 1350 1.4239 0.1333

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0+cu118
  • Datasets 2.15.0
  • Tokenizers 0.15.0