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swin-tiny-patch4-window7-224-finetuned-wuhan

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update model card README.md

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@@ -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: 1.0
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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: 0.0203
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- - Accuracy: 1.0
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  ## Model description
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@@ -61,62 +61,32 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 50
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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 | 3 | 0.6245 | 0.7778 |
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- | No log | 2.0 | 6 | 0.5321 | 0.7778 |
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- | No log | 3.0 | 9 | 0.5123 | 0.7778 |
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- | 0.6482 | 4.0 | 12 | 0.4956 | 0.7778 |
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- | 0.6482 | 5.0 | 15 | 0.4585 | 0.7778 |
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- | 0.6482 | 6.0 | 18 | 0.3743 | 0.8611 |
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- | 0.5574 | 7.0 | 21 | 0.2842 | 0.9167 |
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- | 0.5574 | 8.0 | 24 | 0.2125 | 0.9167 |
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- | 0.5574 | 9.0 | 27 | 0.2683 | 0.9167 |
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- | 0.4882 | 10.0 | 30 | 0.1316 | 0.9444 |
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- | 0.4882 | 11.0 | 33 | 0.1366 | 0.9444 |
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- | 0.4882 | 12.0 | 36 | 0.0745 | 0.9722 |
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- | 0.4882 | 13.0 | 39 | 0.1065 | 0.9444 |
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- | 0.0907 | 14.0 | 42 | 0.0477 | 0.9722 |
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- | 0.0907 | 15.0 | 45 | 0.0460 | 0.9444 |
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- | 0.0907 | 16.0 | 48 | 0.0438 | 0.9722 |
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- | 0.0481 | 17.0 | 51 | 0.0203 | 1.0 |
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- | 0.0481 | 18.0 | 54 | 0.0093 | 1.0 |
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- | 0.0481 | 19.0 | 57 | 0.0082 | 1.0 |
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- | 0.013 | 20.0 | 60 | 0.0017 | 1.0 |
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- | 0.013 | 21.0 | 63 | 0.0008 | 1.0 |
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- | 0.013 | 22.0 | 66 | 0.0002 | 1.0 |
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- | 0.013 | 23.0 | 69 | 0.0001 | 1.0 |
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- | 0.0101 | 24.0 | 72 | 0.0938 | 0.9722 |
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- | 0.0101 | 25.0 | 75 | 0.1019 | 0.9722 |
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- | 0.0101 | 26.0 | 78 | 0.0005 | 1.0 |
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- | 0.0085 | 27.0 | 81 | 0.0000 | 1.0 |
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- | 0.0085 | 28.0 | 84 | 0.0000 | 1.0 |
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- | 0.0085 | 29.0 | 87 | 0.0001 | 1.0 |
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- | 0.0196 | 30.0 | 90 | 0.0001 | 1.0 |
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- | 0.0196 | 31.0 | 93 | 0.0001 | 1.0 |
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- | 0.0196 | 32.0 | 96 | 0.0000 | 1.0 |
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- | 0.0196 | 33.0 | 99 | 0.0000 | 1.0 |
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- | 0.0027 | 34.0 | 102 | 0.0000 | 1.0 |
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- | 0.0027 | 35.0 | 105 | 0.0000 | 1.0 |
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- | 0.0027 | 36.0 | 108 | 0.0000 | 1.0 |
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- | 0.0016 | 37.0 | 111 | 0.0000 | 1.0 |
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- | 0.0016 | 38.0 | 114 | 0.0000 | 1.0 |
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- | 0.0016 | 39.0 | 117 | 0.0000 | 1.0 |
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- | 0.0021 | 40.0 | 120 | 0.0000 | 1.0 |
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- | 0.0021 | 41.0 | 123 | 0.0000 | 1.0 |
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- | 0.0021 | 42.0 | 126 | 0.0000 | 1.0 |
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- | 0.0021 | 43.0 | 129 | 0.0000 | 1.0 |
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- | 0.0024 | 44.0 | 132 | 0.0000 | 1.0 |
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- | 0.0024 | 45.0 | 135 | 0.0000 | 1.0 |
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- | 0.0024 | 46.0 | 138 | 0.0000 | 1.0 |
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- | 0.0009 | 47.0 | 141 | 0.0000 | 1.0 |
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- | 0.0009 | 48.0 | 144 | 0.0000 | 1.0 |
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- | 0.0009 | 49.0 | 147 | 0.0000 | 1.0 |
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- | 0.0006 | 50.0 | 150 | 0.0000 | 1.0 |
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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: 0.2222222222222222
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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: 6.1948
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+ - Accuracy: 0.2222
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  ## Model description
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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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  - 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 | 3 | 0.7953 | 0.4 |
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+ | No log | 2.0 | 6 | 0.9477 | 0.4 |
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+ | No log | 3.0 | 9 | 1.0106 | 0.4 |
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+ | 0.5883 | 4.0 | 12 | 1.4170 | 0.4 |
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+ | 0.5883 | 5.0 | 15 | 1.7436 | 0.4 |
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+ | 0.5883 | 6.0 | 18 | 2.5380 | 0.4 |
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+ | 0.241 | 7.0 | 21 | 3.8803 | 0.4 |
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+ | 0.241 | 8.0 | 24 | 2.4040 | 0.2222 |
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+ | 0.241 | 9.0 | 27 | 3.9968 | 0.4 |
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+ | 0.125 | 10.0 | 30 | 3.2731 | 0.4 |
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+ | 0.125 | 11.0 | 33 | 3.2202 | 0.2222 |
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+ | 0.125 | 12.0 | 36 | 4.7008 | 0.4 |
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+ | 0.125 | 13.0 | 39 | 4.5588 | 0.3556 |
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+ | 0.0766 | 14.0 | 42 | 4.5434 | 0.2444 |
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+ | 0.0766 | 15.0 | 45 | 4.9792 | 0.2667 |
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+ | 0.0766 | 16.0 | 48 | 5.4095 | 0.2667 |
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+ | 0.0239 | 17.0 | 51 | 5.8507 | 0.2222 |
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+ | 0.0239 | 18.0 | 54 | 6.1023 | 0.2222 |
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+ | 0.0239 | 19.0 | 57 | 6.1666 | 0.2222 |
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+ | 0.0129 | 20.0 | 60 | 6.1948 | 0.2222 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions