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BilalMuftuoglu
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deit-base-distilled-patch16-224-65-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-patch16-224-65-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: 0.8732394366197183

deit-base-distilled-patch16-224-65-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: 0.4993
  • Accuracy: 0.8732

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 0.9231 3 0.7403 0.5493
No log 1.8462 6 0.7199 0.5211
No log 2.7692 9 0.7111 0.5634
0.7693 4.0 13 0.7015 0.5352
0.7693 4.9231 16 0.6471 0.6197
0.7693 5.8462 19 0.6691 0.6056
0.6542 6.7692 22 0.6188 0.6197
0.6542 8.0 26 0.6967 0.5775
0.6542 8.9231 29 0.5732 0.7324
0.5935 9.8462 32 0.5184 0.7042
0.5935 10.7692 35 0.6031 0.7183
0.5935 12.0 39 0.6671 0.6479
0.549 12.9231 42 0.5281 0.7183
0.549 13.8462 45 0.5792 0.7183
0.549 14.7692 48 0.5389 0.7465
0.4778 16.0 52 0.6010 0.7042
0.4778 16.9231 55 0.5245 0.7606
0.4778 17.8462 58 0.5491 0.7183
0.4039 18.7692 61 0.5590 0.7465
0.4039 20.0 65 0.4886 0.7324
0.4039 20.9231 68 0.5050 0.7324
0.3409 21.8462 71 0.4912 0.7465
0.3409 22.7692 74 0.4929 0.7746
0.3409 24.0 78 0.5365 0.7746
0.3202 24.9231 81 0.4685 0.8028
0.3202 25.8462 84 0.4404 0.8169
0.3202 26.7692 87 0.4639 0.8028
0.2466 28.0 91 0.5491 0.7606
0.2466 28.9231 94 0.5170 0.7606
0.2466 29.8462 97 0.4444 0.8028
0.2433 30.7692 100 0.4517 0.8310
0.2433 32.0 104 0.7797 0.7606
0.2433 32.9231 107 0.4321 0.8169
0.2535 33.8462 110 0.5956 0.7746
0.2535 34.7692 113 0.4695 0.7887
0.2535 36.0 117 0.8106 0.6901
0.2215 36.9231 120 0.7119 0.7465
0.2215 37.8462 123 0.4752 0.8028
0.2215 38.7692 126 0.4784 0.8169
0.2143 40.0 130 0.4773 0.8028
0.2143 40.9231 133 0.5581 0.8169
0.2143 41.8462 136 0.6098 0.8028
0.2143 42.7692 139 0.5193 0.8169
0.1726 44.0 143 0.4306 0.8451
0.1726 44.9231 146 0.4234 0.8592
0.1726 45.8462 149 0.5264 0.8169
0.1684 46.7692 152 0.7303 0.8028
0.1684 48.0 156 0.5079 0.8169
0.1684 48.9231 159 0.5392 0.8169
0.1604 49.8462 162 0.3951 0.8169
0.1604 50.7692 165 0.4311 0.8028
0.1604 52.0 169 0.4082 0.8028
0.1457 52.9231 172 0.4173 0.7887
0.1457 53.8462 175 0.4311 0.8310
0.1457 54.7692 178 0.4213 0.8028
0.1549 56.0 182 0.4713 0.8451
0.1549 56.9231 185 0.7493 0.8028
0.1549 57.8462 188 0.5161 0.8451
0.1391 58.7692 191 0.4685 0.8169
0.1391 60.0 195 0.6968 0.8028
0.1391 60.9231 198 0.5837 0.8310
0.1272 61.8462 201 0.5863 0.8169
0.1272 62.7692 204 0.5460 0.8310
0.1272 64.0 208 0.6198 0.8310
0.1341 64.9231 211 0.5584 0.8592
0.1341 65.8462 214 0.6429 0.8451
0.1341 66.7692 217 0.8592 0.8028
0.1144 68.0 221 0.8472 0.8028
0.1144 68.9231 224 0.8360 0.8169
0.1144 69.8462 227 0.6697 0.8169
0.1321 70.7692 230 0.6625 0.8028
0.1321 72.0 234 0.7228 0.8310
0.1321 72.9231 237 0.6793 0.8310
0.1206 73.8462 240 0.5571 0.8592
0.1206 74.7692 243 0.5106 0.8451
0.1206 76.0 247 0.6686 0.8310
0.131 76.9231 250 0.7132 0.8310
0.131 77.8462 253 0.5945 0.8451
0.131 78.7692 256 0.5516 0.7746
0.1009 80.0 260 0.5474 0.7606
0.1009 80.9231 263 0.5219 0.7887
0.1009 81.8462 266 0.5375 0.8451
0.1009 82.7692 269 0.5133 0.8451
0.1084 84.0 273 0.4911 0.8451
0.1084 84.9231 276 0.4993 0.8732
0.1084 85.8462 279 0.5418 0.8592
0.0851 86.7692 282 0.6010 0.8451
0.0851 88.0 286 0.6305 0.8451
0.0851 88.9231 289 0.6016 0.8451
0.1071 89.8462 292 0.5773 0.8592
0.1071 90.7692 295 0.5610 0.8732
0.1071 92.0 299 0.5522 0.8732
0.1139 92.3077 300 0.5514 0.8732

Framework versions

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