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BilalMuftuoglu
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deit-base-distilled-patch16-224-85-fold3

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metadata
license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: deit-base-distilled-patch16-224-85-fold3
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9090909090909091

deit-base-distilled-patch16-224-85-fold3

This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3477
  • Accuracy: 0.9091

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 2 0.7892 0.3409
No log 2.0 4 0.5546 0.7727
No log 3.0 6 0.6493 0.7727
No log 4.0 8 0.6648 0.7727
0.6939 5.0 10 0.5187 0.7727
0.6939 6.0 12 0.4903 0.8182
0.6939 7.0 14 0.5087 0.7955
0.6939 8.0 16 0.5789 0.7727
0.6939 9.0 18 0.4919 0.8409
0.4553 10.0 20 0.4707 0.75
0.4553 11.0 22 0.5120 0.8182
0.4553 12.0 24 0.4734 0.75
0.4553 13.0 26 0.4255 0.7727
0.4553 14.0 28 0.3695 0.8636
0.3658 15.0 30 0.3848 0.8182
0.3658 16.0 32 0.3586 0.8409
0.3658 17.0 34 0.4962 0.8409
0.3658 18.0 36 0.3645 0.8636
0.3658 19.0 38 0.3455 0.8864
0.2667 20.0 40 0.3477 0.9091
0.2667 21.0 42 0.3275 0.8864
0.2667 22.0 44 0.3400 0.8864
0.2667 23.0 46 0.3780 0.8864
0.2667 24.0 48 0.4243 0.8409
0.1794 25.0 50 0.4429 0.8409
0.1794 26.0 52 0.5026 0.8409
0.1794 27.0 54 0.4811 0.8409
0.1794 28.0 56 0.4733 0.8182
0.1794 29.0 58 0.4384 0.8636
0.1861 30.0 60 0.4354 0.9091
0.1861 31.0 62 0.4511 0.8864
0.1861 32.0 64 0.3315 0.8636
0.1861 33.0 66 0.3100 0.8864
0.1861 34.0 68 0.3594 0.9091
0.1521 35.0 70 0.4052 0.9091
0.1521 36.0 72 0.3878 0.8864
0.1521 37.0 74 0.3905 0.9091
0.1521 38.0 76 0.4173 0.9091
0.1521 39.0 78 0.4774 0.9091
0.1333 40.0 80 0.5656 0.8864
0.1333 41.0 82 0.5146 0.9091
0.1333 42.0 84 0.4158 0.8636
0.1333 43.0 86 0.4067 0.8636
0.1333 44.0 88 0.4412 0.9091
0.1297 45.0 90 0.4733 0.9091
0.1297 46.0 92 0.4243 0.9091
0.1297 47.0 94 0.4279 0.9091
0.1297 48.0 96 0.4020 0.9091
0.1297 49.0 98 0.3842 0.8636
0.1038 50.0 100 0.3811 0.8409
0.1038 51.0 102 0.3947 0.8636
0.1038 52.0 104 0.4587 0.9091
0.1038 53.0 106 0.4300 0.9091
0.1038 54.0 108 0.3804 0.8636
0.1101 55.0 110 0.4216 0.8636
0.1101 56.0 112 0.3966 0.8636
0.1101 57.0 114 0.4216 0.9091
0.1101 58.0 116 0.4569 0.9091
0.1101 59.0 118 0.4392 0.9091
0.1085 60.0 120 0.4479 0.9091
0.1085 61.0 122 0.4657 0.9091
0.1085 62.0 124 0.5242 0.9091
0.1085 63.0 126 0.5626 0.9091
0.1085 64.0 128 0.5570 0.9091
0.105 65.0 130 0.5035 0.9091
0.105 66.0 132 0.4490 0.9091
0.105 67.0 134 0.4366 0.9091
0.105 68.0 136 0.4416 0.8636
0.105 69.0 138 0.4597 0.9091
0.0918 70.0 140 0.4795 0.8636
0.0918 71.0 142 0.4922 0.8636
0.0918 72.0 144 0.5078 0.8409
0.0918 73.0 146 0.5089 0.8636
0.0918 74.0 148 0.5109 0.8636
0.1072 75.0 150 0.5125 0.8864
0.1072 76.0 152 0.5267 0.8864
0.1072 77.0 154 0.5346 0.9091
0.1072 78.0 156 0.5291 0.8864
0.1072 79.0 158 0.5188 0.8636
0.0895 80.0 160 0.5222 0.8636
0.0895 81.0 162 0.5319 0.8636
0.0895 82.0 164 0.5475 0.8864
0.0895 83.0 166 0.5576 0.9091
0.0895 84.0 168 0.5441 0.9091
0.0836 85.0 170 0.5266 0.8864
0.0836 86.0 172 0.5047 0.8864
0.0836 87.0 174 0.4888 0.8864
0.0836 88.0 176 0.4824 0.8864
0.0836 89.0 178 0.4814 0.8864
0.0996 90.0 180 0.4823 0.9091
0.0996 91.0 182 0.4826 0.9091
0.0996 92.0 184 0.4841 0.8864
0.0996 93.0 186 0.4880 0.9091
0.0996 94.0 188 0.4879 0.9091
0.086 95.0 190 0.4829 0.9091
0.086 96.0 192 0.4798 0.8864
0.086 97.0 194 0.4811 0.8864
0.086 98.0 196 0.4819 0.8864
0.086 99.0 198 0.4816 0.8864
0.0745 100.0 200 0.4816 0.8864

Framework versions

  • Transformers 4.41.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1