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mountaincreek12
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swin-tiny-patch4-window7-224-finetuned-sealv1

Image Classification
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swin
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  1. README.md +32 -32
  2. model.safetensors +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: 0.9793103448275862
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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/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0650
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- - Accuracy: 0.9793
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  ## Model description
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@@ -67,35 +67,35 @@ 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 | 0.95 | 5 | 1.2920 | 0.4966 |
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- | 1.1379 | 1.9 | 10 | 1.0177 | 0.4966 |
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- | 1.1379 | 2.86 | 15 | 0.7626 | 0.8759 |
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- | 0.6784 | 4.0 | 21 | 0.5388 | 0.9310 |
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- | 0.6784 | 4.95 | 26 | 0.4191 | 0.9103 |
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- | 0.3269 | 5.9 | 31 | 0.3990 | 0.8897 |
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- | 0.3269 | 6.86 | 36 | 0.2090 | 0.9517 |
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- | 0.2068 | 8.0 | 42 | 0.1819 | 0.9586 |
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- | 0.2068 | 8.95 | 47 | 0.1192 | 0.9655 |
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- | 0.1104 | 9.9 | 52 | 0.0682 | 0.9724 |
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- | 0.1104 | 10.86 | 57 | 0.0854 | 0.9724 |
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- | 0.0571 | 12.0 | 63 | 0.0816 | 0.9655 |
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- | 0.0571 | 12.95 | 68 | 0.0535 | 0.9793 |
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- | 0.0382 | 13.9 | 73 | 0.0491 | 0.9793 |
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- | 0.0382 | 14.86 | 78 | 0.0534 | 0.9793 |
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- | 0.0158 | 16.0 | 84 | 0.0369 | 0.9793 |
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- | 0.0158 | 16.95 | 89 | 0.1111 | 0.9724 |
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- | 0.0082 | 17.9 | 94 | 0.0515 | 0.9862 |
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- | 0.0082 | 18.86 | 99 | 0.0713 | 0.9793 |
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- | 0.0105 | 20.0 | 105 | 0.0598 | 0.9793 |
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- | 0.009 | 20.95 | 110 | 0.0759 | 0.9724 |
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- | 0.009 | 21.9 | 115 | 0.0769 | 0.9793 |
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- | 0.0134 | 22.86 | 120 | 0.0702 | 0.9793 |
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- | 0.0134 | 24.0 | 126 | 0.0605 | 0.9793 |
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- | 0.0042 | 24.95 | 131 | 0.0621 | 0.9793 |
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- | 0.0042 | 25.9 | 136 | 0.0654 | 0.9793 |
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- | 0.0027 | 26.86 | 141 | 0.0666 | 0.9724 |
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- | 0.0027 | 28.0 | 147 | 0.0665 | 0.9793 |
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- | 0.0065 | 28.57 | 150 | 0.0650 | 0.9793 |
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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: 0.946969696969697
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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/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4069
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+ - Accuracy: 0.9470
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.95 | 9 | 1.1667 | 0.6023 |
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+ | 1.2777 | 2.0 | 19 | 0.9542 | 0.8864 |
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+ | 0.8743 | 2.95 | 28 | 0.5694 | 0.9015 |
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+ | 0.5282 | 4.0 | 38 | 0.3682 | 0.9129 |
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+ | 0.2988 | 4.95 | 47 | 0.2135 | 0.9545 |
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+ | 0.1832 | 6.0 | 57 | 0.2820 | 0.9167 |
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+ | 0.1867 | 6.95 | 66 | 0.1944 | 0.9432 |
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+ | 0.1077 | 8.0 | 76 | 0.2345 | 0.9432 |
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+ | 0.0571 | 8.95 | 85 | 0.2389 | 0.9470 |
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+ | 0.0379 | 10.0 | 95 | 0.2260 | 0.9432 |
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+ | 0.0233 | 10.95 | 104 | 0.2329 | 0.9432 |
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+ | 0.0163 | 12.0 | 114 | 0.2610 | 0.9356 |
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+ | 0.019 | 12.95 | 123 | 0.3660 | 0.9508 |
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+ | 0.0113 | 14.0 | 133 | 0.2777 | 0.9470 |
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+ | 0.0084 | 14.95 | 142 | 0.3123 | 0.9508 |
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+ | 0.008 | 16.0 | 152 | 0.3222 | 0.9470 |
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+ | 0.0048 | 16.95 | 161 | 0.3232 | 0.9470 |
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+ | 0.0075 | 18.0 | 171 | 0.3476 | 0.9508 |
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+ | 0.0048 | 18.95 | 180 | 0.3304 | 0.9470 |
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+ | 0.0143 | 20.0 | 190 | 0.4560 | 0.9432 |
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+ | 0.0143 | 20.95 | 199 | 0.3720 | 0.9432 |
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+ | 0.0019 | 22.0 | 209 | 0.3579 | 0.9394 |
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+ | 0.0063 | 22.95 | 218 | 0.4064 | 0.9432 |
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+ | 0.0023 | 24.0 | 228 | 0.4741 | 0.9394 |
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+ | 0.0015 | 24.95 | 237 | 0.4111 | 0.9470 |
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+ | 0.0022 | 26.0 | 247 | 0.3914 | 0.9432 |
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+ | 0.0008 | 26.95 | 256 | 0.3945 | 0.9432 |
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+ | 0.0024 | 28.0 | 266 | 0.4053 | 0.9470 |
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+ | 0.0026 | 28.42 | 270 | 0.4069 | 0.9470 |
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  ### Framework versions
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