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desarrolloasesoreslocales
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cvt-13-normal

Image Classification
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cvt
Generated from Trainer
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@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.5415944540727903
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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
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7170
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- - Accuracy: 0.5416
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  ## Model description
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@@ -53,7 +53,7 @@ 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: 3e-05
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  - train_batch_size: 128
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  - eval_batch_size: 128
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  - seed: 42
@@ -62,32 +62,112 @@ The following hyperparameters were used during training:
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 20
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 7 | 2.1186 | 0.4653 |
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- | 9.0717 | 2.0 | 14 | 2.0551 | 0.4757 |
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- | 9.0021 | 3.0 | 21 | 2.0214 | 0.4818 |
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- | 9.0021 | 4.0 | 28 | 1.9844 | 0.4931 |
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- | 8.6494 | 5.0 | 35 | 1.9556 | 0.4913 |
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- | 8.5946 | 6.0 | 42 | 1.9194 | 0.4974 |
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- | 8.5946 | 7.0 | 49 | 1.8840 | 0.5035 |
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- | 8.5009 | 8.0 | 56 | 1.8537 | 0.5199 |
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- | 8.3451 | 9.0 | 63 | 1.8305 | 0.5217 |
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- | 8.3695 | 10.0 | 70 | 1.8097 | 0.5277 |
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- | 8.3695 | 11.0 | 77 | 1.7975 | 0.5260 |
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- | 8.1806 | 12.0 | 84 | 1.7794 | 0.5312 |
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- | 8.1791 | 13.0 | 91 | 1.7645 | 0.5364 |
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- | 8.1791 | 14.0 | 98 | 1.7583 | 0.5355 |
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- | 8.1636 | 15.0 | 105 | 1.7438 | 0.5399 |
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- | 8.0585 | 16.0 | 112 | 1.7325 | 0.5407 |
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- | 8.0585 | 17.0 | 119 | 1.7263 | 0.5407 |
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- | 8.0785 | 18.0 | 126 | 1.7186 | 0.5381 |
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- | 7.9187 | 19.0 | 133 | 1.7135 | 0.5416 |
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- | 8.0204 | 20.0 | 140 | 1.7170 | 0.5416 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.7954939341421143
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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/cvt-13](https://huggingface.co/microsoft/cvt-13) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8635
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+ - Accuracy: 0.7955
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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: 0.0003
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  - train_batch_size: 128
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  - eval_batch_size: 128
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  - seed: 42
 
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  - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 100
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 7 | 1.0210 | 0.7106 |
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+ | 5.5642 | 2.0 | 14 | 1.0072 | 0.7097 |
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+ | 5.662 | 3.0 | 21 | 1.0151 | 0.7088 |
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+ | 5.662 | 4.0 | 28 | 1.0016 | 0.7140 |
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+ | 5.381 | 5.0 | 35 | 1.0119 | 0.7123 |
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+ | 5.3348 | 6.0 | 42 | 0.9662 | 0.7201 |
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+ | 5.3348 | 7.0 | 49 | 0.9514 | 0.7262 |
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+ | 5.2423 | 8.0 | 56 | 0.9589 | 0.7106 |
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+ | 5.0251 | 9.0 | 63 | 0.9090 | 0.7279 |
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+ | 5.0547 | 10.0 | 70 | 0.9352 | 0.7123 |
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+ | 5.0547 | 11.0 | 77 | 1.0063 | 0.6993 |
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+ | 4.8246 | 12.0 | 84 | 0.9191 | 0.7106 |
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+ | 4.7811 | 13.0 | 91 | 0.9947 | 0.7123 |
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+ | 4.7811 | 14.0 | 98 | 0.9671 | 0.7175 |
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+ | 4.8234 | 15.0 | 105 | 0.9055 | 0.7236 |
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+ | 4.4787 | 16.0 | 112 | 0.8838 | 0.7444 |
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+ | 4.4787 | 17.0 | 119 | 0.9059 | 0.7296 |
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+ | 4.39 | 18.0 | 126 | 0.8640 | 0.7461 |
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+ | 4.1424 | 19.0 | 133 | 0.8661 | 0.7487 |
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+ | 4.1065 | 20.0 | 140 | 0.9057 | 0.7305 |
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+ | 4.1065 | 21.0 | 147 | 0.8865 | 0.7348 |
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+ | 4.0844 | 22.0 | 154 | 0.8928 | 0.7392 |
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+ | 3.9835 | 23.0 | 161 | 0.8675 | 0.7539 |
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+ | 3.9835 | 24.0 | 168 | 0.8829 | 0.7556 |
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+ | 3.8199 | 25.0 | 175 | 0.8177 | 0.7617 |
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+ | 3.7898 | 26.0 | 182 | 0.8886 | 0.7461 |
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+ | 3.7898 | 27.0 | 189 | 0.9395 | 0.7461 |
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+ | 3.7734 | 28.0 | 196 | 0.8348 | 0.7608 |
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+ | 3.7835 | 29.0 | 203 | 0.8369 | 0.7574 |
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+ | 3.6414 | 30.0 | 210 | 0.8668 | 0.7660 |
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+ | 3.6414 | 31.0 | 217 | 0.8909 | 0.7600 |
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+ | 3.5076 | 32.0 | 224 | 0.8795 | 0.7496 |
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+ | 3.5447 | 33.0 | 231 | 0.9228 | 0.7539 |
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+ | 3.5447 | 34.0 | 238 | 0.8850 | 0.7522 |
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+ | 3.5344 | 35.0 | 245 | 0.8585 | 0.7652 |
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+ | 3.3678 | 36.0 | 252 | 0.8631 | 0.7574 |
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+ | 3.3678 | 37.0 | 259 | 0.8676 | 0.7704 |
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+ | 3.4061 | 38.0 | 266 | 0.9131 | 0.7617 |
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+ | 3.3177 | 39.0 | 273 | 0.8631 | 0.7678 |
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+ | 3.2767 | 40.0 | 280 | 0.8802 | 0.7643 |
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+ | 3.2767 | 41.0 | 287 | 0.8518 | 0.7678 |
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+ | 3.1992 | 42.0 | 294 | 0.9232 | 0.7574 |
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+ | 3.2743 | 43.0 | 301 | 0.9306 | 0.7522 |
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+ | 3.2743 | 44.0 | 308 | 0.8420 | 0.7756 |
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+ | 3.1704 | 45.0 | 315 | 0.8802 | 0.7565 |
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+ | 3.2466 | 46.0 | 322 | 0.8782 | 0.7678 |
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+ | 3.2466 | 47.0 | 329 | 0.8444 | 0.7747 |
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+ | 3.0879 | 48.0 | 336 | 0.8579 | 0.7695 |
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+ | 3.1677 | 49.0 | 343 | 0.8584 | 0.7712 |
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+ | 3.0965 | 50.0 | 350 | 0.8401 | 0.7756 |
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+ | 3.0965 | 51.0 | 357 | 0.8724 | 0.7652 |
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+ | 3.0611 | 52.0 | 364 | 0.8638 | 0.7808 |
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+ | 3.0204 | 53.0 | 371 | 0.9167 | 0.7660 |
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+ | 3.0204 | 54.0 | 378 | 0.8322 | 0.7738 |
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+ | 2.9704 | 55.0 | 385 | 0.8577 | 0.7643 |
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+ | 2.939 | 56.0 | 392 | 0.8297 | 0.7860 |
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+ | 2.939 | 57.0 | 399 | 0.8746 | 0.7686 |
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+ | 3.0341 | 58.0 | 406 | 0.8620 | 0.7825 |
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+ | 2.8997 | 59.0 | 413 | 0.8835 | 0.7574 |
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+ | 3.0187 | 60.0 | 420 | 0.9018 | 0.7695 |
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+ | 3.0187 | 61.0 | 427 | 0.8940 | 0.7773 |
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+ | 2.9316 | 62.0 | 434 | 0.8859 | 0.7712 |
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+ | 2.8746 | 63.0 | 441 | 0.8661 | 0.7764 |
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+ | 2.8746 | 64.0 | 448 | 0.8916 | 0.7712 |
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+ | 2.817 | 65.0 | 455 | 0.8645 | 0.7782 |
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+ | 2.7593 | 66.0 | 462 | 0.8829 | 0.7686 |
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+ | 2.7593 | 67.0 | 469 | 0.8883 | 0.7790 |
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+ | 2.9212 | 68.0 | 476 | 0.8507 | 0.7825 |
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+ | 2.8659 | 69.0 | 483 | 0.8554 | 0.7877 |
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+ | 2.9068 | 70.0 | 490 | 0.8813 | 0.7764 |
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+ | 2.9068 | 71.0 | 497 | 0.8555 | 0.7860 |
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+ | 2.8334 | 72.0 | 504 | 0.8666 | 0.7790 |
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+ | 2.7322 | 73.0 | 511 | 0.8682 | 0.7825 |
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+ | 2.7322 | 74.0 | 518 | 0.8816 | 0.7886 |
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+ | 2.8548 | 75.0 | 525 | 0.8523 | 0.7903 |
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+ | 2.8696 | 76.0 | 532 | 0.8509 | 0.7894 |
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+ | 2.8696 | 77.0 | 539 | 0.8683 | 0.7808 |
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+ | 2.6439 | 78.0 | 546 | 0.8607 | 0.7877 |
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+ | 2.9039 | 79.0 | 553 | 0.8698 | 0.7842 |
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+ | 2.6338 | 80.0 | 560 | 0.8718 | 0.7877 |
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+ | 2.6338 | 81.0 | 567 | 0.8371 | 0.7903 |
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+ | 2.7271 | 82.0 | 574 | 0.8427 | 0.7929 |
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+ | 2.7555 | 83.0 | 581 | 0.8622 | 0.7938 |
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+ | 2.7555 | 84.0 | 588 | 0.8769 | 0.7860 |
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+ | 2.7702 | 85.0 | 595 | 0.8844 | 0.7860 |
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+ | 2.8678 | 86.0 | 602 | 0.8882 | 0.7825 |
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+ | 2.8678 | 87.0 | 609 | 0.8716 | 0.7825 |
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+ | 2.6334 | 88.0 | 616 | 0.8782 | 0.7782 |
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+ | 2.7782 | 89.0 | 623 | 0.8752 | 0.7808 |
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+ | 2.5527 | 90.0 | 630 | 0.8675 | 0.7808 |
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+ | 2.5527 | 91.0 | 637 | 0.8735 | 0.7842 |
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+ | 2.6812 | 92.0 | 644 | 0.8650 | 0.7886 |
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+ | 2.6167 | 93.0 | 651 | 0.8531 | 0.7946 |
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+ | 2.6167 | 94.0 | 658 | 0.8699 | 0.7868 |
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+ | 2.6553 | 95.0 | 665 | 0.8667 | 0.7894 |
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+ | 2.7758 | 96.0 | 672 | 0.8650 | 0.7920 |
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+ | 2.7758 | 97.0 | 679 | 0.8685 | 0.7903 |
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+ | 2.6592 | 98.0 | 686 | 0.8592 | 0.7886 |
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+ | 2.5202 | 99.0 | 693 | 0.8745 | 0.7894 |
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+ | 2.6577 | 100.0 | 700 | 0.8635 | 0.7955 |
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  ### Framework versions