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hkivancoral
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smids_3x_beit_base_sgd_001_fold3

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: 0.36833333333333335
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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: 1.1865
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- - Accuracy: 0.3683
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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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- | 1.2808 | 1.0 | 225 | 1.3240 | 0.3133 |
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- | 1.3076 | 2.0 | 450 | 1.3173 | 0.3167 |
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- | 1.2543 | 3.0 | 675 | 1.3110 | 0.3217 |
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- | 1.2448 | 4.0 | 900 | 1.3050 | 0.3217 |
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- | 1.2098 | 5.0 | 1125 | 1.2990 | 0.3233 |
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- | 1.2433 | 6.0 | 1350 | 1.2933 | 0.325 |
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- | 1.235 | 7.0 | 1575 | 1.2877 | 0.325 |
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- | 1.2061 | 8.0 | 1800 | 1.2825 | 0.3267 |
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- | 1.1968 | 9.0 | 2025 | 1.2774 | 0.3283 |
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- | 1.235 | 10.0 | 2250 | 1.2725 | 0.3333 |
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- | 1.2386 | 11.0 | 2475 | 1.2679 | 0.335 |
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- | 1.1719 | 12.0 | 2700 | 1.2634 | 0.3367 |
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- | 1.2201 | 13.0 | 2925 | 1.2589 | 0.3383 |
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- | 1.2275 | 14.0 | 3150 | 1.2546 | 0.3433 |
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- | 1.1527 | 15.0 | 3375 | 1.2505 | 0.3483 |
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- | 1.1541 | 16.0 | 3600 | 1.2466 | 0.35 |
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- | 1.1452 | 17.0 | 3825 | 1.2427 | 0.3517 |
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- | 1.1632 | 18.0 | 4050 | 1.2391 | 0.355 |
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- | 1.1705 | 19.0 | 4275 | 1.2357 | 0.355 |
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- | 1.1243 | 20.0 | 4500 | 1.2324 | 0.355 |
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- | 1.1714 | 21.0 | 4725 | 1.2292 | 0.3567 |
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- | 1.1212 | 22.0 | 4950 | 1.2260 | 0.3567 |
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- | 1.083 | 23.0 | 5175 | 1.2230 | 0.36 |
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- | 1.1693 | 24.0 | 5400 | 1.2201 | 0.36 |
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- | 1.1429 | 25.0 | 5625 | 1.2174 | 0.3617 |
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- | 1.1524 | 26.0 | 5850 | 1.2148 | 0.3633 |
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- | 1.1809 | 27.0 | 6075 | 1.2124 | 0.36 |
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- | 1.1029 | 28.0 | 6300 | 1.2100 | 0.36 |
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- | 1.1631 | 29.0 | 6525 | 1.2078 | 0.3583 |
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- | 1.1132 | 30.0 | 6750 | 1.2057 | 0.3583 |
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- | 1.1547 | 31.0 | 6975 | 1.2038 | 0.3583 |
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- | 1.1369 | 32.0 | 7200 | 1.2019 | 0.3583 |
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- | 1.1212 | 33.0 | 7425 | 1.2002 | 0.3583 |
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- | 1.0988 | 34.0 | 7650 | 1.1985 | 0.36 |
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- | 1.1451 | 35.0 | 7875 | 1.1970 | 0.3633 |
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- | 1.1505 | 36.0 | 8100 | 1.1956 | 0.3633 |
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- | 1.1035 | 37.0 | 8325 | 1.1943 | 0.3633 |
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- | 1.0903 | 38.0 | 8550 | 1.1930 | 0.3633 |
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- | 1.1088 | 39.0 | 8775 | 1.1919 | 0.3633 |
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- | 1.1231 | 40.0 | 9000 | 1.1910 | 0.3633 |
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- | 1.1064 | 41.0 | 9225 | 1.1901 | 0.3633 |
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- | 1.1536 | 42.0 | 9450 | 1.1893 | 0.3667 |
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- | 1.1441 | 43.0 | 9675 | 1.1886 | 0.3667 |
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- | 1.1599 | 44.0 | 9900 | 1.1880 | 0.3683 |
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- | 1.123 | 45.0 | 10125 | 1.1875 | 0.3683 |
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- | 1.0819 | 46.0 | 10350 | 1.1872 | 0.3683 |
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- | 1.0934 | 47.0 | 10575 | 1.1869 | 0.3683 |
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- | 1.1542 | 48.0 | 10800 | 1.1867 | 0.3683 |
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- | 1.1456 | 49.0 | 11025 | 1.1866 | 0.3683 |
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- | 1.1179 | 50.0 | 11250 | 1.1865 | 0.3683 |
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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: 0.8966666666666666
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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: 0.2937
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+ - Accuracy: 0.8967
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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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+ | 0.8635 | 1.0 | 225 | 0.8411 | 0.6267 |
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+ | 0.6523 | 2.0 | 450 | 0.6173 | 0.7583 |
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+ | 0.5403 | 3.0 | 675 | 0.5212 | 0.8017 |
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+ | 0.4896 | 4.0 | 900 | 0.4692 | 0.8233 |
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+ | 0.4594 | 5.0 | 1125 | 0.4353 | 0.8333 |
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+ | 0.4326 | 6.0 | 1350 | 0.4063 | 0.8467 |
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+ | 0.3692 | 7.0 | 1575 | 0.3897 | 0.855 |
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+ | 0.4088 | 8.0 | 1800 | 0.3756 | 0.8567 |
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+ | 0.4036 | 9.0 | 2025 | 0.3600 | 0.8667 |
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+ | 0.387 | 10.0 | 2250 | 0.3535 | 0.8717 |
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+ | 0.349 | 11.0 | 2475 | 0.3460 | 0.8717 |
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+ | 0.3537 | 12.0 | 2700 | 0.3401 | 0.875 |
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+ | 0.3714 | 13.0 | 2925 | 0.3342 | 0.8783 |
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+ | 0.3497 | 14.0 | 3150 | 0.3327 | 0.8817 |
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+ | 0.2955 | 15.0 | 3375 | 0.3234 | 0.8867 |
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+ | 0.346 | 16.0 | 3600 | 0.3197 | 0.8933 |
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+ | 0.3452 | 17.0 | 3825 | 0.3164 | 0.89 |
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+ | 0.3182 | 18.0 | 4050 | 0.3143 | 0.8867 |
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+ | 0.3047 | 19.0 | 4275 | 0.3110 | 0.8933 |
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+ | 0.3008 | 20.0 | 4500 | 0.3105 | 0.8883 |
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+ | 0.2783 | 21.0 | 4725 | 0.3050 | 0.8917 |
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+ | 0.2751 | 22.0 | 4950 | 0.3037 | 0.8967 |
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+ | 0.2477 | 23.0 | 5175 | 0.3059 | 0.8917 |
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+ | 0.2485 | 24.0 | 5400 | 0.3040 | 0.8917 |
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+ | 0.2841 | 25.0 | 5625 | 0.3099 | 0.8917 |
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+ | 0.2803 | 26.0 | 5850 | 0.3058 | 0.8967 |
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+ | 0.2313 | 27.0 | 6075 | 0.3019 | 0.8933 |
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+ | 0.2302 | 28.0 | 6300 | 0.3005 | 0.895 |
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+ | 0.2775 | 29.0 | 6525 | 0.2994 | 0.895 |
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+ | 0.2039 | 30.0 | 6750 | 0.2961 | 0.9 |
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+ | 0.261 | 31.0 | 6975 | 0.2949 | 0.9 |
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+ | 0.2791 | 32.0 | 7200 | 0.2986 | 0.895 |
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+ | 0.2917 | 33.0 | 7425 | 0.2938 | 0.8983 |
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+ | 0.2364 | 34.0 | 7650 | 0.2966 | 0.895 |
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+ | 0.2087 | 35.0 | 7875 | 0.2917 | 0.9 |
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+ | 0.2544 | 36.0 | 8100 | 0.2944 | 0.8983 |
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+ | 0.2254 | 37.0 | 8325 | 0.2941 | 0.8967 |
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+ | 0.2119 | 38.0 | 8550 | 0.2972 | 0.8933 |
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+ | 0.2445 | 39.0 | 8775 | 0.2905 | 0.9 |
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+ | 0.204 | 40.0 | 9000 | 0.2909 | 0.8967 |
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+ | 0.2353 | 41.0 | 9225 | 0.2968 | 0.895 |
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+ | 0.2574 | 42.0 | 9450 | 0.2926 | 0.9 |
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+ | 0.2197 | 43.0 | 9675 | 0.2953 | 0.8967 |
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+ | 0.2519 | 44.0 | 9900 | 0.2939 | 0.8967 |
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+ | 0.2337 | 45.0 | 10125 | 0.2971 | 0.895 |
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+ | 0.2047 | 46.0 | 10350 | 0.2932 | 0.8967 |
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+ | 0.2633 | 47.0 | 10575 | 0.2935 | 0.8967 |
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+ | 0.2254 | 48.0 | 10800 | 0.2947 | 0.895 |
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+ | 0.2679 | 49.0 | 11025 | 0.2937 | 0.895 |
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+ | 0.2687 | 50.0 | 11250 | 0.2937 | 0.8967 |
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  ### Framework versions
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