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swin-tiny-patch4-window7-224-finetuned-agrivision

Image Classification
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PyTorch
swin
Generated from Trainer
Eval Results
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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9362186788154897
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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
@@ -31,8 +31,8 @@ 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: 0.2783
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- - Accuracy: 0.9362
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  ## Model description
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@@ -66,36 +66,36 @@ 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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- | 0.5829 | 1.0 | 31 | 0.7480 | 0.7267 |
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- | 0.1199 | 2.0 | 62 | 0.4407 | 0.8246 |
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- | 0.1028 | 3.0 | 93 | 0.4477 | 0.8246 |
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- | 0.0533 | 4.0 | 124 | 0.4606 | 0.8292 |
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- | 0.0411 | 5.0 | 155 | 0.2470 | 0.9180 |
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- | 0.022 | 6.0 | 186 | 0.1568 | 0.9544 |
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- | 0.0206 | 7.0 | 217 | 0.4187 | 0.8793 |
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- | 0.0069 | 8.0 | 248 | 0.2498 | 0.9203 |
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- | 0.0053 | 9.0 | 279 | 0.2654 | 0.9226 |
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- | 0.0094 | 10.0 | 310 | 0.2343 | 0.9385 |
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- | 0.0152 | 11.0 | 341 | 0.3421 | 0.9021 |
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- | 0.0047 | 12.0 | 372 | 0.4494 | 0.8724 |
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- | 0.0128 | 13.0 | 403 | 0.5360 | 0.8679 |
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- | 0.0024 | 14.0 | 434 | 0.2775 | 0.9112 |
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- | 0.0127 | 15.0 | 465 | 0.2911 | 0.8975 |
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- | 0.0038 | 16.0 | 496 | 0.2337 | 0.9294 |
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- | 0.0001 | 17.0 | 527 | 0.2207 | 0.9408 |
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- | 0.0054 | 18.0 | 558 | 0.2506 | 0.9362 |
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- | 0.0011 | 19.0 | 589 | 0.3778 | 0.8952 |
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- | 0.0002 | 20.0 | 620 | 0.2316 | 0.9408 |
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- | 0.0003 | 21.0 | 651 | 0.2133 | 0.9431 |
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- | 0.0009 | 22.0 | 682 | 0.2519 | 0.9339 |
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- | 0.0004 | 23.0 | 713 | 0.2931 | 0.9203 |
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- | 0.0001 | 24.0 | 744 | 0.2847 | 0.9271 |
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- | 0.0003 | 25.0 | 775 | 0.2831 | 0.9317 |
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- | 0.0008 | 26.0 | 806 | 0.2919 | 0.9271 |
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- | 0.0003 | 27.0 | 837 | 0.2798 | 0.9362 |
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- | 0.0008 | 28.0 | 868 | 0.2857 | 0.9362 |
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- | 0.0008 | 29.0 | 899 | 0.2780 | 0.9362 |
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- | 0.0013 | 30.0 | 930 | 0.2783 | 0.9362 |
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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: 0.9202733485193622
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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: 0.3605
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+ - Accuracy: 0.9203
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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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+ | 0.5913 | 1.0 | 31 | 0.7046 | 0.7175 |
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+ | 0.1409 | 2.0 | 62 | 0.8423 | 0.6788 |
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+ | 0.0825 | 3.0 | 93 | 0.6224 | 0.7654 |
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+ | 0.0509 | 4.0 | 124 | 0.4379 | 0.8360 |
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+ | 0.0439 | 5.0 | 155 | 0.1706 | 0.9317 |
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+ | 0.0107 | 6.0 | 186 | 0.1914 | 0.9362 |
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+ | 0.0134 | 7.0 | 217 | 0.2491 | 0.9089 |
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+ | 0.0338 | 8.0 | 248 | 0.2119 | 0.9362 |
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+ | 0.0306 | 9.0 | 279 | 0.4502 | 0.8610 |
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+ | 0.0054 | 10.0 | 310 | 0.4990 | 0.8747 |
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+ | 0.0033 | 11.0 | 341 | 0.2746 | 0.9112 |
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+ | 0.0021 | 12.0 | 372 | 0.2501 | 0.9317 |
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+ | 0.0068 | 13.0 | 403 | 0.1883 | 0.9522 |
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+ | 0.0038 | 14.0 | 434 | 0.3672 | 0.9134 |
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+ | 0.0006 | 15.0 | 465 | 0.2275 | 0.9408 |
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+ | 0.0011 | 16.0 | 496 | 0.3349 | 0.9134 |
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+ | 0.0017 | 17.0 | 527 | 0.3329 | 0.9157 |
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+ | 0.0007 | 18.0 | 558 | 0.2508 | 0.9317 |
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+ | 0.0023 | 19.0 | 589 | 0.2338 | 0.9385 |
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+ | 0.0003 | 20.0 | 620 | 0.3193 | 0.9226 |
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+ | 0.002 | 21.0 | 651 | 0.4604 | 0.9043 |
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+ | 0.0023 | 22.0 | 682 | 0.3338 | 0.9203 |
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+ | 0.005 | 23.0 | 713 | 0.2925 | 0.9271 |
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+ | 0.0001 | 24.0 | 744 | 0.2022 | 0.9522 |
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+ | 0.0002 | 25.0 | 775 | 0.2699 | 0.9339 |
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+ | 0.0007 | 26.0 | 806 | 0.2603 | 0.9385 |
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+ | 0.0005 | 27.0 | 837 | 0.4120 | 0.9134 |
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+ | 0.0003 | 28.0 | 868 | 0.3550 | 0.9203 |
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+ | 0.0008 | 29.0 | 899 | 0.3657 | 0.9203 |
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+ | 0.0 | 30.0 | 930 | 0.3605 | 0.9203 |
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  ### Framework versions