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resnet-50-bottomCleanedData

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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: 0.8342792281498297
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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/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4809
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- - Accuracy: 0.8343
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  ## Model description
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@@ -60,22 +60,62 @@ 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: 0.01
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- - num_epochs: 10
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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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- | 1.3235 | 1.0 | 141 | 1.3266 | 0.5096 |
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- | 1.1546 | 2.0 | 283 | 1.1380 | 0.5153 |
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- | 0.9412 | 2.99 | 424 | 0.8690 | 0.6515 |
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- | 0.8539 | 4.0 | 566 | 0.6672 | 0.7594 |
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- | 0.7967 | 4.99 | 707 | 0.6256 | 0.7503 |
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- | 0.7679 | 6.0 | 849 | 0.5357 | 0.8229 |
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- | 0.7265 | 7.0 | 991 | 0.5698 | 0.7832 |
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- | 0.7395 | 8.0 | 1132 | 0.5125 | 0.8161 |
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- | 0.7029 | 9.0 | 1274 | 0.4993 | 0.8150 |
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- | 0.7275 | 9.96 | 1410 | 0.4809 | 0.8343 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.9761634506242906
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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/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0813
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+ - Accuracy: 0.9762
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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: 0.01
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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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+ | 1.3323 | 1.0 | 141 | 1.3319 | 0.5187 |
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+ | 1.1302 | 2.0 | 283 | 1.1059 | 0.5335 |
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+ | 0.8793 | 2.99 | 424 | 0.7848 | 0.7094 |
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+ | 0.7652 | 4.0 | 566 | 0.7255 | 0.7219 |
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+ | 0.7708 | 4.99 | 707 | 0.5280 | 0.8173 |
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+ | 0.6153 | 6.0 | 849 | 0.4221 | 0.8490 |
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+ | 0.5895 | 7.0 | 991 | 0.4015 | 0.8570 |
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+ | 0.5617 | 8.0 | 1132 | 0.2998 | 0.9001 |
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+ | 0.517 | 9.0 | 1274 | 0.2737 | 0.9160 |
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+ | 0.5366 | 9.99 | 1415 | 0.2229 | 0.9240 |
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+ | 0.4645 | 11.0 | 1557 | 0.2038 | 0.9330 |
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+ | 0.4114 | 11.99 | 1698 | 0.1851 | 0.9376 |
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+ | 0.4528 | 13.0 | 1840 | 0.1796 | 0.9432 |
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+ | 0.4182 | 14.0 | 1982 | 0.1578 | 0.9523 |
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+ | 0.432 | 15.0 | 2123 | 0.1660 | 0.9421 |
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+ | 0.4442 | 16.0 | 2265 | 0.1401 | 0.9557 |
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+ | 0.4059 | 16.99 | 2406 | 0.1332 | 0.9591 |
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+ | 0.3498 | 18.0 | 2548 | 0.1431 | 0.9535 |
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+ | 0.3869 | 18.99 | 2689 | 0.1237 | 0.9512 |
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+ | 0.3639 | 20.0 | 2831 | 0.1193 | 0.9603 |
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+ | 0.3819 | 21.0 | 2973 | 0.1234 | 0.9557 |
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+ | 0.3491 | 22.0 | 3114 | 0.1207 | 0.9569 |
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+ | 0.3259 | 23.0 | 3256 | 0.1234 | 0.9591 |
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+ | 0.3199 | 23.99 | 3397 | 0.1028 | 0.9659 |
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+ | 0.3398 | 25.0 | 3539 | 0.1010 | 0.9603 |
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+ | 0.3108 | 25.99 | 3680 | 0.1015 | 0.9671 |
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+ | 0.3417 | 27.0 | 3822 | 0.1080 | 0.9614 |
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+ | 0.3835 | 28.0 | 3964 | 0.1056 | 0.9591 |
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+ | 0.3336 | 29.0 | 4105 | 0.1011 | 0.9637 |
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+ | 0.3035 | 30.0 | 4247 | 0.0972 | 0.9614 |
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+ | 0.2559 | 30.99 | 4388 | 0.0941 | 0.9659 |
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+ | 0.378 | 32.0 | 4530 | 0.0963 | 0.9603 |
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+ | 0.2932 | 32.99 | 4671 | 0.0916 | 0.9716 |
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+ | 0.3072 | 34.0 | 4813 | 0.0917 | 0.9671 |
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+ | 0.3081 | 35.0 | 4955 | 0.1025 | 0.9625 |
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+ | 0.2724 | 36.0 | 5096 | 0.0874 | 0.9671 |
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+ | 0.2621 | 37.0 | 5238 | 0.0847 | 0.9705 |
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+ | 0.3521 | 37.99 | 5379 | 0.0829 | 0.9728 |
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+ | 0.2883 | 39.0 | 5521 | 0.0860 | 0.9728 |
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+ | 0.2617 | 39.99 | 5662 | 0.0898 | 0.9682 |
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+ | 0.2893 | 41.0 | 5804 | 0.0877 | 0.9671 |
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+ | 0.2994 | 42.0 | 5946 | 0.0822 | 0.9762 |
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+ | 0.2483 | 43.0 | 6087 | 0.0834 | 0.9705 |
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+ | 0.301 | 44.0 | 6229 | 0.0883 | 0.9694 |
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+ | 0.2648 | 44.99 | 6370 | 0.0834 | 0.9705 |
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+ | 0.2902 | 46.0 | 6512 | 0.0879 | 0.9648 |
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+ | 0.299 | 46.99 | 6653 | 0.0843 | 0.9694 |
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+ | 0.2726 | 48.0 | 6795 | 0.0920 | 0.9659 |
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+ | 0.3252 | 49.0 | 6937 | 0.0857 | 0.9716 |
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+ | 0.274 | 49.8 | 7050 | 0.0813 | 0.9762 |
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  ### Framework versions