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vit-large-ai-or-not

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  1. README.md +42 -16
  2. model.safetensors +1 -1
  3. runs/Oct23_10-04-38_b03069d3f47e/events.out.tfevents.1729677921.b03069d3f47e.444.1 +2 -2
README.md CHANGED
@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1039
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- - Accuracy: 0.9581
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  ## Model description
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@@ -38,30 +38,56 @@ 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: 0.0002
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- - train_batch_size: 16
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 2
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  - mixed_precision_training: Native AMP
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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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- | 0.3925 | 0.1808 | 200 | 0.4045 | 0.8786 |
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- | 0.2803 | 0.3617 | 400 | 0.2386 | 0.9044 |
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- | 0.2235 | 0.5425 | 600 | 0.1893 | 0.9173 |
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- | 0.217 | 0.7233 | 800 | 0.1597 | 0.9398 |
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- | 0.1865 | 0.9042 | 1000 | 0.1413 | 0.9420 |
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- | 0.1309 | 1.0850 | 1200 | 0.1474 | 0.9517 |
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- | 0.1008 | 1.2658 | 1400 | 0.1914 | 0.9420 |
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- | 0.0793 | 1.4467 | 1600 | 0.1557 | 0.9441 |
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- | 0.0804 | 1.6275 | 1800 | 0.2301 | 0.9313 |
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- | 0.0814 | 1.8083 | 2000 | 0.1039 | 0.9581 |
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- | 0.0446 | 1.9892 | 2200 | 0.1124 | 0.9635 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1641
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+ - Accuracy: 0.9761
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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: 1e-05
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+ - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 4
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  - mixed_precision_training: Native AMP
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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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+ | 0.1089 | 0.1074 | 200 | 0.1756 | 0.9498 |
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+ | 0.041 | 0.2148 | 400 | 0.2151 | 0.9503 |
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+ | 0.0566 | 0.3222 | 600 | 0.2125 | 0.9511 |
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+ | 0.1028 | 0.4296 | 800 | 0.2084 | 0.9450 |
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+ | 0.1722 | 0.5371 | 1000 | 0.1658 | 0.9557 |
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+ | 0.1486 | 0.6445 | 1200 | 0.1312 | 0.9595 |
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+ | 0.1446 | 0.7519 | 1400 | 0.1634 | 0.9565 |
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+ | 0.1281 | 0.8593 | 1600 | 0.1282 | 0.9654 |
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+ | 0.1584 | 0.9667 | 1800 | 0.1295 | 0.9667 |
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+ | 0.0549 | 1.0741 | 2000 | 0.1613 | 0.9670 |
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+ | 0.0373 | 1.1815 | 2200 | 0.1344 | 0.9723 |
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+ | 0.0293 | 1.2889 | 2400 | 0.1584 | 0.9699 |
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+ | 0.0251 | 1.3963 | 2600 | 0.1704 | 0.9656 |
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+ | 0.0249 | 1.5038 | 2800 | 0.1586 | 0.9699 |
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+ | 0.0383 | 1.6112 | 3000 | 0.1467 | 0.9715 |
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+ | 0.0213 | 1.7186 | 3200 | 0.1546 | 0.9734 |
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+ | 0.0544 | 1.8260 | 3400 | 0.1671 | 0.9686 |
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+ | 0.0401 | 1.9334 | 3600 | 0.1870 | 0.9656 |
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+ | 0.0288 | 2.0408 | 3800 | 0.1981 | 0.9600 |
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+ | 0.0078 | 2.1482 | 4000 | 0.1422 | 0.9748 |
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+ | 0.0037 | 2.2556 | 4200 | 0.1775 | 0.9705 |
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+ | 0.0035 | 2.3631 | 4400 | 0.1845 | 0.9705 |
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+ | 0.0043 | 2.4705 | 4600 | 0.2001 | 0.9710 |
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+ | 0.0049 | 2.5779 | 4800 | 0.2145 | 0.9689 |
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+ | 0.01 | 2.6853 | 5000 | 0.1445 | 0.9750 |
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+ | 0.0039 | 2.7927 | 5200 | 0.1509 | 0.9748 |
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+ | 0.0055 | 2.9001 | 5400 | 0.1674 | 0.9748 |
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+ | 0.0094 | 3.0075 | 5600 | 0.1569 | 0.9748 |
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+ | 0.0018 | 3.1149 | 5800 | 0.1580 | 0.9753 |
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+ | 0.0 | 3.2223 | 6000 | 0.1698 | 0.9761 |
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+ | 0.0003 | 3.3298 | 6200 | 0.1606 | 0.9761 |
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+ | 0.0034 | 3.4372 | 6400 | 0.1870 | 0.9737 |
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+ | 0.0 | 3.5446 | 6600 | 0.1697 | 0.9756 |
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+ | 0.0 | 3.6520 | 6800 | 0.1673 | 0.9750 |
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+ | 0.0053 | 3.7594 | 7000 | 0.1644 | 0.9753 |
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+ | 0.0 | 3.8668 | 7200 | 0.1676 | 0.9753 |
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+ | 0.0013 | 3.9742 | 7400 | 0.1641 | 0.9761 |
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  ### Framework versions
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