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BilalMuftuoglu
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deit-base-distilled-patch16-224-55-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-patch16-224-55-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: 0.8227848101265823

deit-base-distilled-patch16-224-55-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: 0.4529
  • Accuracy: 0.8228

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 0.8571 3 0.8367 0.4051
No log 2.0 7 0.7223 0.4557
0.7025 2.8571 10 0.7199 0.4684
0.7025 4.0 14 0.6096 0.7089
0.7025 4.8571 17 0.6278 0.5823
0.6356 6.0 21 0.5629 0.7089
0.6356 6.8571 24 0.5924 0.6835
0.6356 8.0 28 0.5365 0.7722
0.5493 8.8571 31 0.6082 0.6329
0.5493 10.0 35 0.7239 0.5949
0.5493 10.8571 38 0.5435 0.7722
0.5205 12.0 42 0.8530 0.5570
0.5205 12.8571 45 0.5530 0.6709
0.5205 14.0 49 0.4728 0.7722
0.4979 14.8571 52 0.9571 0.5570
0.4979 16.0 56 0.5193 0.7722
0.4979 16.8571 59 0.4529 0.8228
0.4957 18.0 63 0.4686 0.7975
0.4957 18.8571 66 0.5060 0.7722
0.3659 20.0 70 0.4821 0.7848
0.3659 20.8571 73 0.6116 0.7089
0.3659 22.0 77 0.5860 0.7215
0.2973 22.8571 80 0.7100 0.7089
0.2973 24.0 84 0.6446 0.7342
0.2973 24.8571 87 0.6294 0.7342
0.2647 26.0 91 0.5988 0.7342
0.2647 26.8571 94 0.5256 0.7342
0.2647 28.0 98 0.6628 0.7595
0.2527 28.8571 101 0.5054 0.7595
0.2527 30.0 105 0.7632 0.7595
0.2527 30.8571 108 0.5917 0.7848
0.2176 32.0 112 0.5293 0.7848
0.2176 32.8571 115 0.6048 0.7468
0.2176 34.0 119 0.5710 0.7468
0.1633 34.8571 122 0.5901 0.7595
0.1633 36.0 126 0.8161 0.7468
0.1633 36.8571 129 0.7202 0.7468
0.1753 38.0 133 0.8239 0.7215
0.1753 38.8571 136 0.8908 0.7215
0.1743 40.0 140 0.8519 0.7342
0.1743 40.8571 143 1.0071 0.7215
0.1743 42.0 147 0.7842 0.7342
0.1532 42.8571 150 0.7827 0.7089
0.1532 44.0 154 0.7150 0.7468
0.1532 44.8571 157 0.6905 0.7595
0.1526 46.0 161 0.9260 0.7089
0.1526 46.8571 164 0.7933 0.7595
0.1526 48.0 168 0.8580 0.7468
0.1519 48.8571 171 0.6899 0.7975
0.1519 50.0 175 0.7069 0.7848
0.1519 50.8571 178 0.6741 0.7595
0.1292 52.0 182 0.7183 0.7848
0.1292 52.8571 185 0.8051 0.7468
0.1292 54.0 189 0.6883 0.7722
0.1305 54.8571 192 0.8266 0.7468
0.1305 56.0 196 1.0871 0.7595
0.1305 56.8571 199 0.7595 0.7595
0.1129 58.0 203 0.6880 0.7595
0.1129 58.8571 206 1.0676 0.7595
0.1369 60.0 210 0.8078 0.7595
0.1369 60.8571 213 0.7850 0.7595
0.1369 62.0 217 0.6975 0.7722
0.127 62.8571 220 0.7212 0.7595
0.127 64.0 224 0.8967 0.7468
0.127 64.8571 227 1.0046 0.7595
0.1238 66.0 231 0.8611 0.7342
0.1238 66.8571 234 0.9676 0.7975
0.1238 68.0 238 1.3115 0.7215
0.1068 68.8571 241 1.0992 0.7468
0.1068 70.0 245 0.8765 0.7848
0.1068 70.8571 248 0.8510 0.7848
0.1019 72.0 252 0.7403 0.7975
0.1019 72.8571 255 0.7459 0.7975
0.1019 74.0 259 0.7705 0.7975
0.1002 74.8571 262 0.7535 0.7975
0.1002 76.0 266 0.7124 0.7722
0.1002 76.8571 269 0.7014 0.7342
0.1222 78.0 273 0.8068 0.7722
0.1222 78.8571 276 0.9451 0.7722
0.1091 80.0 280 1.0048 0.7848
0.1091 80.8571 283 0.9518 0.7722
0.1091 82.0 287 0.8575 0.7848
0.0957 82.8571 290 0.8441 0.7848
0.0957 84.0 294 0.8602 0.7848
0.0957 84.8571 297 0.8701 0.7848
0.1111 85.7143 300 0.8731 0.7848

Framework versions

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