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End of training
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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: hushem_1x_deit_tiny_sgd_lr001_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: 0.3111111111111111

hushem_1x_deit_tiny_sgd_lr001_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: 1.4305
  • Accuracy: 0.3111

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
No log 1.0 6 1.6118 0.1778
1.6924 2.0 12 1.5735 0.2222
1.6924 3.0 18 1.5416 0.2222
1.5478 4.0 24 1.5201 0.2444
1.5093 5.0 30 1.4995 0.2889
1.5093 6.0 36 1.4836 0.2889
1.4614 7.0 42 1.4737 0.2889
1.4614 8.0 48 1.4656 0.2889
1.3895 9.0 54 1.4578 0.2222
1.4002 10.0 60 1.4519 0.2667
1.4002 11.0 66 1.4464 0.2667
1.3595 12.0 72 1.4429 0.2667
1.3595 13.0 78 1.4392 0.2667
1.3506 14.0 84 1.4366 0.2222
1.2804 15.0 90 1.4347 0.2
1.2804 16.0 96 1.4330 0.2
1.2746 17.0 102 1.4333 0.2667
1.2746 18.0 108 1.4332 0.2667
1.2774 19.0 114 1.4327 0.2667
1.2547 20.0 120 1.4313 0.2667
1.2547 21.0 126 1.4295 0.2667
1.2313 22.0 132 1.4282 0.2889
1.2313 23.0 138 1.4285 0.2889
1.2194 24.0 144 1.4285 0.2889
1.2083 25.0 150 1.4272 0.2889
1.2083 26.0 156 1.4286 0.3111
1.1973 27.0 162 1.4278 0.3111
1.1973 28.0 168 1.4278 0.3111
1.1964 29.0 174 1.4276 0.3111
1.2006 30.0 180 1.4293 0.3111
1.2006 31.0 186 1.4290 0.3111
1.1662 32.0 192 1.4295 0.3111
1.1662 33.0 198 1.4297 0.3111
1.1889 34.0 204 1.4294 0.3111
1.1683 35.0 210 1.4293 0.3111
1.1683 36.0 216 1.4299 0.3111
1.1652 37.0 222 1.4302 0.3111
1.1652 38.0 228 1.4307 0.3111
1.1321 39.0 234 1.4308 0.3111
1.1584 40.0 240 1.4306 0.3111
1.1584 41.0 246 1.4304 0.3111
1.1553 42.0 252 1.4305 0.3111
1.1553 43.0 258 1.4305 0.3111
1.168 44.0 264 1.4305 0.3111
1.1533 45.0 270 1.4305 0.3111
1.1533 46.0 276 1.4305 0.3111
1.1682 47.0 282 1.4305 0.3111
1.1682 48.0 288 1.4305 0.3111
1.1255 49.0 294 1.4305 0.3111
1.1698 50.0 300 1.4305 0.3111

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1