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SW2-DMAE

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swinv2
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  1. README.md +37 -37
  2. model.safetensors +1 -1
  3. runs/Dec03_17-16-06_DESKTOP-SKBE9FB/events.out.tfevents.1733267767.DESKTOP-SKBE9FB.6452.0 +2 -2
README.md CHANGED
@@ -32,7 +32,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.6712
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  - Accuracy: 0.4565
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  ## Model description
@@ -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: 4e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -67,41 +67,41 @@ 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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- | No log | 0.86 | 3 | 7.9402 | 0.1087 |
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- | No log | 2.0 | 7 | 7.9103 | 0.1087 |
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- | 7.944 | 2.86 | 10 | 7.8273 | 0.1087 |
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- | 7.944 | 4.0 | 14 | 7.4556 | 0.1087 |
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- | 7.944 | 4.86 | 17 | 7.0147 | 0.1087 |
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- | 7.2884 | 6.0 | 21 | 6.3290 | 0.1087 |
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- | 7.2884 | 6.86 | 24 | 5.8362 | 0.1087 |
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- | 7.2884 | 8.0 | 28 | 5.1955 | 0.1087 |
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- | 5.6818 | 8.86 | 31 | 4.7393 | 0.1087 |
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- | 5.6818 | 10.0 | 35 | 4.1698 | 0.1087 |
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- | 5.6818 | 10.86 | 38 | 3.7678 | 0.1087 |
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- | 4.3923 | 12.0 | 42 | 3.2683 | 0.1087 |
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- | 4.3923 | 12.86 | 45 | 2.9314 | 0.1087 |
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- | 4.3923 | 14.0 | 49 | 2.5362 | 0.1087 |
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- | 3.0666 | 14.86 | 52 | 2.2842 | 0.1087 |
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- | 3.0666 | 16.0 | 56 | 2.0075 | 0.1087 |
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- | 3.0666 | 16.86 | 59 | 1.8426 | 0.1087 |
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- | 2.1462 | 18.0 | 63 | 1.6712 | 0.4565 |
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- | 2.1462 | 18.86 | 66 | 1.5719 | 0.4565 |
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- | 1.6612 | 20.0 | 70 | 1.4659 | 0.4565 |
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- | 1.6612 | 20.86 | 73 | 1.4003 | 0.4565 |
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- | 1.6612 | 22.0 | 77 | 1.3301 | 0.4565 |
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- | 1.3809 | 22.86 | 80 | 1.2896 | 0.4565 |
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- | 1.3809 | 24.0 | 84 | 1.2505 | 0.4565 |
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- | 1.3809 | 24.86 | 87 | 1.2316 | 0.4565 |
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- | 1.2478 | 26.0 | 91 | 1.2170 | 0.4565 |
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- | 1.2478 | 26.86 | 94 | 1.2122 | 0.4565 |
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- | 1.2478 | 28.0 | 98 | 1.2116 | 0.4565 |
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- | 1.2118 | 28.86 | 101 | 1.2110 | 0.4565 |
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- | 1.2118 | 30.0 | 105 | 1.2108 | 0.4565 |
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- | 1.2118 | 30.86 | 108 | 1.2102 | 0.4565 |
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- | 1.1877 | 32.0 | 112 | 1.2095 | 0.4565 |
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- | 1.1877 | 32.86 | 115 | 1.2091 | 0.4565 |
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- | 1.1877 | 34.0 | 119 | 1.2088 | 0.4565 |
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- | 1.213 | 34.29 | 120 | 1.2088 | 0.4565 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2072
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  - Accuracy: 0.4565
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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: 5e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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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+ | No log | 0.86 | 3 | 7.9394 | 0.1087 |
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+ | No log | 2.0 | 7 | 7.8979 | 0.1087 |
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+ | 7.935 | 2.86 | 10 | 7.7672 | 0.1087 |
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+ | 7.935 | 4.0 | 14 | 7.2197 | 0.1087 |
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+ | 7.935 | 4.86 | 17 | 6.5661 | 0.1087 |
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+ | 7.0143 | 6.0 | 21 | 5.7304 | 0.1087 |
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+ | 7.0143 | 6.86 | 24 | 5.1184 | 0.1087 |
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+ | 7.0143 | 8.0 | 28 | 4.3526 | 0.1087 |
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+ | 4.9972 | 8.86 | 31 | 3.8117 | 0.1087 |
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+ | 4.9972 | 10.0 | 35 | 3.1518 | 0.1087 |
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+ | 4.9972 | 10.86 | 38 | 2.7125 | 0.1087 |
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+ | 3.3803 | 12.0 | 42 | 2.2254 | 0.1087 |
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+ | 3.3803 | 12.86 | 45 | 1.9450 | 0.1087 |
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+ | 3.3803 | 14.0 | 49 | 1.6739 | 0.4565 |
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+ | 2.0759 | 14.86 | 52 | 1.5299 | 0.4565 |
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+ | 2.0759 | 16.0 | 56 | 1.3876 | 0.4565 |
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+ | 2.0759 | 16.86 | 59 | 1.3059 | 0.4565 |
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+ | 1.4466 | 18.0 | 63 | 1.2341 | 0.4565 |
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+ | 1.4466 | 18.86 | 66 | 1.2120 | 0.4565 |
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+ | 1.2349 | 20.0 | 70 | 1.2096 | 0.4565 |
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+ | 1.2349 | 20.86 | 73 | 1.2118 | 0.4565 |
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+ | 1.2349 | 22.0 | 77 | 1.2114 | 0.4565 |
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+ | 1.1854 | 22.86 | 80 | 1.2141 | 0.4565 |
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+ | 1.1854 | 24.0 | 84 | 1.2117 | 0.4565 |
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+ | 1.1854 | 24.86 | 87 | 1.2102 | 0.4565 |
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+ | 1.1878 | 26.0 | 91 | 1.2076 | 0.4565 |
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+ | 1.1878 | 26.86 | 94 | 1.2083 | 0.4565 |
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+ | 1.1878 | 28.0 | 98 | 1.2130 | 0.4565 |
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+ | 1.1986 | 28.86 | 101 | 1.2069 | 0.4565 |
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+ | 1.1986 | 30.0 | 105 | 1.2058 | 0.4565 |
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+ | 1.1986 | 30.86 | 108 | 1.2070 | 0.4565 |
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+ | 1.182 | 32.0 | 112 | 1.2075 | 0.4565 |
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+ | 1.182 | 32.86 | 115 | 1.2074 | 0.4565 |
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+ | 1.182 | 34.0 | 119 | 1.2072 | 0.4565 |
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+ | 1.2064 | 34.29 | 120 | 1.2072 | 0.4565 |
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  ### Framework versions
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