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End of training
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metadata
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_base_rms_0001_fold3
    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: 0.5116279069767442

hushem_1x_deit_base_rms_0001_fold3

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: 2.5728
  • Accuracy: 0.5116

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
No log 1.0 6 1.5927 0.2558
1.8022 2.0 12 1.4288 0.2558
1.8022 3.0 18 1.5308 0.2558
1.5239 4.0 24 1.4740 0.2558
1.4801 5.0 30 2.2308 0.2558
1.4801 6.0 36 1.4153 0.2558
1.5953 7.0 42 1.4884 0.2326
1.5953 8.0 48 1.5775 0.2558
1.4262 9.0 54 1.4549 0.2558
1.4607 10.0 60 1.4401 0.2558
1.4607 11.0 66 1.3707 0.3256
1.3771 12.0 72 1.3285 0.3721
1.3771 13.0 78 1.2980 0.3721
1.2887 14.0 84 1.6464 0.2558
1.194 15.0 90 1.4572 0.2791
1.194 16.0 96 1.5171 0.3256
1.2311 17.0 102 1.4178 0.4186
1.2311 18.0 108 1.3427 0.3256
1.2234 19.0 114 1.3369 0.3721
1.1384 20.0 120 1.4187 0.3721
1.1384 21.0 126 1.5195 0.2558
1.0551 22.0 132 1.5010 0.3488
1.0551 23.0 138 1.4046 0.3721
0.958 24.0 144 1.2991 0.3721
0.9405 25.0 150 1.3687 0.4651
0.9405 26.0 156 1.8052 0.3256
0.7255 27.0 162 1.6391 0.4419
0.7255 28.0 168 1.3419 0.5116
0.6435 29.0 174 1.7678 0.4419
0.4939 30.0 180 2.2022 0.3256
0.4939 31.0 186 1.6603 0.5116
0.4434 32.0 192 1.5952 0.4884
0.4434 33.0 198 1.7642 0.5116
0.2864 34.0 204 2.0069 0.4651
0.1858 35.0 210 2.5370 0.4884
0.1858 36.0 216 2.2076 0.4651
0.1248 37.0 222 2.7233 0.5581
0.1248 38.0 228 2.6548 0.5349
0.0495 39.0 234 2.4864 0.5116
0.0526 40.0 240 2.5601 0.5116
0.0526 41.0 246 2.5731 0.5116
0.0219 42.0 252 2.5728 0.5116
0.0219 43.0 258 2.5728 0.5116
0.0327 44.0 264 2.5728 0.5116
0.0324 45.0 270 2.5728 0.5116
0.0324 46.0 276 2.5728 0.5116
0.0326 47.0 282 2.5728 0.5116
0.0326 48.0 288 2.5728 0.5116
0.0208 49.0 294 2.5728 0.5116
0.0503 50.0 300 2.5728 0.5116

Framework versions

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