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hkivancoral
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smids_3x_beit_base_sgd_001_fold5

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +54 -54
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.38166666666666665
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1851
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- - Accuracy: 0.3817
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  ## Model description
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@@ -52,7 +52,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -65,56 +65,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 1.2835 | 1.0 | 225 | 1.3130 | 0.3167 |
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- | 1.3011 | 2.0 | 450 | 1.3069 | 0.3167 |
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- | 1.243 | 3.0 | 675 | 1.3010 | 0.3217 |
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- | 1.2411 | 4.0 | 900 | 1.2953 | 0.325 |
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- | 1.2229 | 5.0 | 1125 | 1.2898 | 0.3233 |
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- | 1.2191 | 6.0 | 1350 | 1.2846 | 0.3233 |
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- | 1.2208 | 7.0 | 1575 | 1.2796 | 0.3233 |
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- | 1.1965 | 8.0 | 1800 | 1.2748 | 0.3283 |
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- | 1.2527 | 9.0 | 2025 | 1.2700 | 0.3333 |
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- | 1.2362 | 10.0 | 2250 | 1.2655 | 0.335 |
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- | 1.2197 | 11.0 | 2475 | 1.2613 | 0.335 |
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- | 1.2149 | 12.0 | 2700 | 1.2570 | 0.34 |
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- | 1.2002 | 13.0 | 2925 | 1.2530 | 0.3433 |
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- | 1.1732 | 14.0 | 3150 | 1.2491 | 0.3483 |
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- | 1.2252 | 15.0 | 3375 | 1.2454 | 0.35 |
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- | 1.1628 | 16.0 | 3600 | 1.2417 | 0.3533 |
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- | 1.1999 | 17.0 | 3825 | 1.2381 | 0.3583 |
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- | 1.1844 | 18.0 | 4050 | 1.2348 | 0.3617 |
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- | 1.1674 | 19.0 | 4275 | 1.2315 | 0.3617 |
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- | 1.2258 | 20.0 | 4500 | 1.2284 | 0.36 |
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- | 1.1214 | 21.0 | 4725 | 1.2254 | 0.3633 |
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- | 1.151 | 22.0 | 4950 | 1.2225 | 0.365 |
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- | 1.1693 | 23.0 | 5175 | 1.2197 | 0.3667 |
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- | 1.1675 | 24.0 | 5400 | 1.2170 | 0.3667 |
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- | 1.1534 | 25.0 | 5625 | 1.2144 | 0.3667 |
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- | 1.1654 | 26.0 | 5850 | 1.2120 | 0.3667 |
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- | 1.1707 | 27.0 | 6075 | 1.2097 | 0.3683 |
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- | 1.1315 | 28.0 | 6300 | 1.2075 | 0.3683 |
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- | 1.1501 | 29.0 | 6525 | 1.2054 | 0.37 |
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- | 1.1251 | 30.0 | 6750 | 1.2034 | 0.37 |
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- | 1.2017 | 31.0 | 6975 | 1.2016 | 0.3717 |
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- | 1.0794 | 32.0 | 7200 | 1.1998 | 0.3717 |
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- | 1.1172 | 33.0 | 7425 | 1.1981 | 0.3767 |
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- | 1.1136 | 34.0 | 7650 | 1.1965 | 0.38 |
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- | 1.1368 | 35.0 | 7875 | 1.1951 | 0.3817 |
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- | 1.1416 | 36.0 | 8100 | 1.1937 | 0.38 |
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- | 1.0723 | 37.0 | 8325 | 1.1925 | 0.3833 |
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- | 1.0984 | 38.0 | 8550 | 1.1914 | 0.3833 |
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- | 1.0812 | 39.0 | 8775 | 1.1903 | 0.3817 |
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- | 1.1275 | 40.0 | 9000 | 1.1894 | 0.3817 |
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- | 1.1166 | 41.0 | 9225 | 1.1885 | 0.3817 |
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- | 1.1269 | 42.0 | 9450 | 1.1878 | 0.3817 |
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- | 1.1329 | 43.0 | 9675 | 1.1871 | 0.3817 |
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- | 1.1408 | 44.0 | 9900 | 1.1865 | 0.3817 |
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- | 1.1416 | 45.0 | 10125 | 1.1861 | 0.3817 |
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- | 1.1445 | 46.0 | 10350 | 1.1857 | 0.3817 |
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- | 1.1225 | 47.0 | 10575 | 1.1854 | 0.3817 |
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- | 1.1385 | 48.0 | 10800 | 1.1852 | 0.3817 |
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- | 1.1537 | 49.0 | 11025 | 1.1851 | 0.3817 |
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- | 1.1246 | 50.0 | 11250 | 1.1851 | 0.3817 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8783333333333333
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3105
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+ - Accuracy: 0.8783
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.8516 | 1.0 | 225 | 0.8297 | 0.6267 |
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+ | 0.6679 | 2.0 | 450 | 0.6103 | 0.7567 |
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+ | 0.57 | 3.0 | 675 | 0.5223 | 0.7883 |
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+ | 0.4959 | 4.0 | 900 | 0.4753 | 0.8083 |
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+ | 0.4424 | 5.0 | 1125 | 0.4319 | 0.8233 |
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+ | 0.4261 | 6.0 | 1350 | 0.4129 | 0.8283 |
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+ | 0.4396 | 7.0 | 1575 | 0.4075 | 0.8167 |
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+ | 0.4595 | 8.0 | 1800 | 0.3942 | 0.8267 |
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+ | 0.4172 | 9.0 | 2025 | 0.3692 | 0.8367 |
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+ | 0.3688 | 10.0 | 2250 | 0.3605 | 0.8583 |
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+ | 0.4132 | 11.0 | 2475 | 0.3610 | 0.8417 |
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+ | 0.369 | 12.0 | 2700 | 0.3465 | 0.8567 |
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+ | 0.3672 | 13.0 | 2925 | 0.3443 | 0.8517 |
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+ | 0.3409 | 14.0 | 3150 | 0.3437 | 0.855 |
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+ | 0.2695 | 15.0 | 3375 | 0.3370 | 0.8567 |
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+ | 0.311 | 16.0 | 3600 | 0.3373 | 0.8533 |
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+ | 0.3177 | 17.0 | 3825 | 0.3325 | 0.8567 |
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+ | 0.3059 | 18.0 | 4050 | 0.3310 | 0.8567 |
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+ | 0.3295 | 19.0 | 4275 | 0.3271 | 0.8583 |
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+ | 0.3201 | 20.0 | 4500 | 0.3301 | 0.8667 |
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+ | 0.2645 | 21.0 | 4725 | 0.3242 | 0.8683 |
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+ | 0.2497 | 22.0 | 4950 | 0.3240 | 0.8633 |
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+ | 0.2626 | 23.0 | 5175 | 0.3196 | 0.8617 |
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+ | 0.267 | 24.0 | 5400 | 0.3185 | 0.8733 |
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+ | 0.2637 | 25.0 | 5625 | 0.3155 | 0.8733 |
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+ | 0.3416 | 26.0 | 5850 | 0.3155 | 0.8783 |
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+ | 0.3255 | 27.0 | 6075 | 0.3159 | 0.8767 |
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+ | 0.3021 | 28.0 | 6300 | 0.3189 | 0.875 |
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+ | 0.2292 | 29.0 | 6525 | 0.3137 | 0.8783 |
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+ | 0.2207 | 30.0 | 6750 | 0.3185 | 0.8733 |
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+ | 0.2158 | 31.0 | 6975 | 0.3173 | 0.8683 |
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+ | 0.2149 | 32.0 | 7200 | 0.3154 | 0.87 |
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+ | 0.248 | 33.0 | 7425 | 0.3134 | 0.8767 |
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+ | 0.2339 | 34.0 | 7650 | 0.3133 | 0.875 |
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+ | 0.2585 | 35.0 | 7875 | 0.3147 | 0.8767 |
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+ | 0.2565 | 36.0 | 8100 | 0.3120 | 0.875 |
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+ | 0.269 | 37.0 | 8325 | 0.3111 | 0.8783 |
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+ | 0.2546 | 38.0 | 8550 | 0.3139 | 0.8733 |
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+ | 0.2114 | 39.0 | 8775 | 0.3110 | 0.8767 |
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+ | 0.2032 | 40.0 | 9000 | 0.3108 | 0.8767 |
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+ | 0.2376 | 41.0 | 9225 | 0.3108 | 0.8783 |
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+ | 0.2558 | 42.0 | 9450 | 0.3092 | 0.8767 |
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+ | 0.2753 | 43.0 | 9675 | 0.3113 | 0.875 |
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+ | 0.2795 | 44.0 | 9900 | 0.3109 | 0.8767 |
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+ | 0.2412 | 45.0 | 10125 | 0.3113 | 0.8783 |
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+ | 0.2003 | 46.0 | 10350 | 0.3105 | 0.88 |
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+ | 0.2528 | 47.0 | 10575 | 0.3109 | 0.88 |
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+ | 0.2265 | 48.0 | 10800 | 0.3109 | 0.8783 |
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+ | 0.2494 | 49.0 | 11025 | 0.3106 | 0.8783 |
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+ | 0.2763 | 50.0 | 11250 | 0.3105 | 0.8783 |
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  ### Framework versions
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