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resnet-50-resnet50_fashion

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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: 0.8163265306122449
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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: 0.7982
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- - Accuracy: 0.8163
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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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- | 0.6396 | 0.99 | 86 | 0.7625 | 0.7347 |
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- | 0.5646 | 2.0 | 173 | 0.5781 | 0.8349 |
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- | 0.4768 | 2.99 | 259 | 0.4791 | 0.8571 |
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- | 0.4161 | 4.0 | 346 | 0.3866 | 0.8905 |
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- | 0.402 | 4.99 | 432 | 0.3294 | 0.9035 |
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- | 0.369 | 6.0 | 519 | 1.0405 | 0.8924 |
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- | 0.3512 | 7.0 | 606 | 1.4847 | 0.8905 |
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- | 0.3439 | 7.99 | 692 | 0.2820 | 0.9054 |
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- | 0.3306 | 9.0 | 779 | 0.3022 | 0.8850 |
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- | 0.3691 | 9.93 | 860 | 0.7982 | 0.8163 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.9703153988868275
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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: 0.1959
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+ - Accuracy: 0.9703
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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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+ | 0.6532 | 0.99 | 86 | 0.6781 | 0.6345 |
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+ | 0.5407 | 2.0 | 173 | 0.5222 | 0.8590 |
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+ | 0.4086 | 2.99 | 259 | 0.3595 | 0.8924 |
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+ | 0.3449 | 4.0 | 346 | 0.2616 | 0.9184 |
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+ | 0.3518 | 4.99 | 432 | 0.2288 | 0.9443 |
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+ | 0.308 | 6.0 | 519 | 0.2758 | 0.9425 |
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+ | 0.3209 | 7.0 | 606 | 0.3777 | 0.9369 |
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+ | 0.284 | 7.99 | 692 | 0.1704 | 0.9555 |
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+ | 0.2466 | 9.0 | 779 | 0.1571 | 0.9462 |
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+ | 0.3123 | 9.99 | 865 | 0.6492 | 0.9406 |
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+ | 0.2827 | 11.0 | 952 | 0.4968 | 0.9406 |
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+ | 0.2736 | 11.99 | 1038 | 0.1370 | 0.9592 |
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+ | 0.2476 | 13.0 | 1125 | 0.1616 | 0.9499 |
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+ | 0.195 | 14.0 | 1212 | 0.1362 | 0.9610 |
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+ | 0.2536 | 14.99 | 1298 | 0.1298 | 0.9536 |
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+ | 0.2022 | 16.0 | 1385 | 0.7470 | 0.9518 |
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+ | 0.2406 | 16.99 | 1471 | 0.1241 | 0.9647 |
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+ | 0.2019 | 18.0 | 1558 | 0.1278 | 0.9536 |
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+ | 0.2073 | 18.99 | 1644 | 0.1134 | 0.9685 |
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+ | 0.1873 | 20.0 | 1731 | 0.6738 | 0.9629 |
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+ | 0.2446 | 21.0 | 1818 | 0.1033 | 0.9685 |
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+ | 0.1999 | 21.99 | 1904 | 0.1181 | 0.9647 |
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+ | 0.1716 | 23.0 | 1991 | 0.1099 | 0.9610 |
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+ | 0.175 | 23.99 | 2077 | 0.1064 | 0.9740 |
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+ | 0.1962 | 25.0 | 2164 | 0.1174 | 0.9722 |
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+ | 0.1943 | 25.99 | 2250 | 1.0625 | 0.9518 |
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+ | 0.2044 | 27.0 | 2337 | 0.8419 | 0.9573 |
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+ | 0.1835 | 28.0 | 2424 | 0.1112 | 0.9703 |
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+ | 0.191 | 28.99 | 2510 | 0.1142 | 0.9685 |
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+ | 0.1676 | 30.0 | 2597 | 0.1080 | 0.9647 |
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+ | 0.1533 | 30.99 | 2683 | 0.1494 | 0.9647 |
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+ | 0.1991 | 32.0 | 2770 | 0.1000 | 0.9703 |
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+ | 0.1845 | 32.99 | 2856 | 0.0989 | 0.9740 |
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+ | 0.1605 | 34.0 | 2943 | 0.0975 | 0.9685 |
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+ | 0.1928 | 35.0 | 3030 | 0.4555 | 0.9629 |
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+ | 0.1506 | 35.99 | 3116 | 0.1059 | 0.9703 |
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+ | 0.1912 | 37.0 | 3203 | 0.1016 | 0.9647 |
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+ | 0.1689 | 37.99 | 3289 | 0.5421 | 0.9666 |
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+ | 0.1467 | 39.0 | 3376 | 0.1095 | 0.9647 |
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+ | 0.1513 | 39.99 | 3462 | 0.3828 | 0.9703 |
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+ | 0.1768 | 41.0 | 3549 | 0.0945 | 0.9703 |
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+ | 0.1633 | 42.0 | 3636 | 0.2250 | 0.9592 |
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+ | 0.1945 | 42.99 | 3722 | 0.2015 | 0.9685 |
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+ | 0.1896 | 44.0 | 3809 | 0.1114 | 0.9666 |
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+ | 0.1629 | 44.99 | 3895 | 0.0954 | 0.9666 |
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+ | 0.1825 | 46.0 | 3982 | 0.0974 | 0.9740 |
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+ | 0.1664 | 46.99 | 4068 | 0.0939 | 0.9703 |
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+ | 0.1535 | 48.0 | 4155 | 0.0935 | 0.9722 |
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+ | 0.1801 | 49.0 | 4242 | 0.0999 | 0.9703 |
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+ | 0.1502 | 49.67 | 4300 | 0.1959 | 0.9703 |
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  ### Framework versions