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ElenaHsieh
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swin-tiny-patch4-window7-224-finetuned-eurosat

Image Classification
Transformers
Safetensors
swin
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +104 -54
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@@ -20,7 +20,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.5917085427135679
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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
@@ -30,8 +30,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0104
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- - Accuracy: 0.5917
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  ## Model description
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@@ -59,62 +59,112 @@ 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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- | 1.0339 | 1.0 | 28 | 1.0541 | 0.5641 |
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- | 1.0193 | 2.0 | 56 | 1.0464 | 0.5622 |
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- | 1.0348 | 3.0 | 84 | 1.0331 | 0.5691 |
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- | 1.0072 | 4.0 | 112 | 1.0254 | 0.5848 |
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- | 0.9892 | 5.0 | 140 | 1.0121 | 0.5754 |
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- | 0.9379 | 6.0 | 168 | 1.0175 | 0.5810 |
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- | 0.9123 | 7.0 | 196 | 1.0120 | 0.5867 |
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- | 0.8865 | 8.0 | 224 | 1.0104 | 0.5917 |
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- | 0.8668 | 9.0 | 252 | 1.0236 | 0.5873 |
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- | 0.8189 | 10.0 | 280 | 1.0360 | 0.5829 |
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- | 0.7933 | 11.0 | 308 | 1.0395 | 0.5835 |
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- | 0.7765 | 12.0 | 336 | 1.0594 | 0.5729 |
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- | 0.7538 | 13.0 | 364 | 1.0552 | 0.5879 |
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- | 0.7146 | 14.0 | 392 | 1.0620 | 0.5829 |
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- | 0.6885 | 15.0 | 420 | 1.0783 | 0.5842 |
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- | 0.6556 | 16.0 | 448 | 1.1010 | 0.5817 |
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- | 0.6453 | 17.0 | 476 | 1.1131 | 0.5735 |
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- | 0.6175 | 18.0 | 504 | 1.1074 | 0.5892 |
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- | 0.5993 | 19.0 | 532 | 1.1328 | 0.5741 |
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- | 0.5683 | 20.0 | 560 | 1.1423 | 0.5791 |
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- | 0.5524 | 21.0 | 588 | 1.1517 | 0.5873 |
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- | 0.5151 | 22.0 | 616 | 1.1673 | 0.5766 |
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- | 0.5096 | 23.0 | 644 | 1.1760 | 0.5798 |
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- | 0.4937 | 24.0 | 672 | 1.1931 | 0.5817 |
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- | 0.487 | 25.0 | 700 | 1.2084 | 0.5735 |
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- | 0.4597 | 26.0 | 728 | 1.2270 | 0.5716 |
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- | 0.4482 | 27.0 | 756 | 1.2389 | 0.5829 |
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- | 0.4183 | 28.0 | 784 | 1.2430 | 0.5773 |
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- | 0.4228 | 29.0 | 812 | 1.2637 | 0.5741 |
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- | 0.4116 | 30.0 | 840 | 1.2688 | 0.5779 |
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- | 0.3942 | 31.0 | 868 | 1.2986 | 0.5879 |
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- | 0.3815 | 32.0 | 896 | 1.2911 | 0.5766 |
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- | 0.3828 | 33.0 | 924 | 1.3113 | 0.5773 |
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- | 0.3791 | 34.0 | 952 | 1.3317 | 0.5766 |
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- | 0.3701 | 35.0 | 980 | 1.3384 | 0.5773 |
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- | 0.3566 | 36.0 | 1008 | 1.3406 | 0.5754 |
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- | 0.3551 | 37.0 | 1036 | 1.3410 | 0.5766 |
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- | 0.3487 | 38.0 | 1064 | 1.3364 | 0.5867 |
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- | 0.3463 | 39.0 | 1092 | 1.3496 | 0.5810 |
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- | 0.3242 | 40.0 | 1120 | 1.3640 | 0.5747 |
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- | 0.3308 | 41.0 | 1148 | 1.3627 | 0.5716 |
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- | 0.3255 | 42.0 | 1176 | 1.3795 | 0.5804 |
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- | 0.3295 | 43.0 | 1204 | 1.3747 | 0.5798 |
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- | 0.3147 | 44.0 | 1232 | 1.3747 | 0.5861 |
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- | 0.3125 | 45.0 | 1260 | 1.3839 | 0.5817 |
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- | 0.3276 | 46.0 | 1288 | 1.3806 | 0.5842 |
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- | 0.2989 | 47.0 | 1316 | 1.3906 | 0.5886 |
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- | 0.2941 | 48.0 | 1344 | 1.3876 | 0.5867 |
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- | 0.3131 | 49.0 | 1372 | 1.3896 | 0.5823 |
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- | 0.2975 | 50.0 | 1400 | 1.3906 | 0.5835 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.9816143497757848
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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 was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0603
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+ - Accuracy: 0.9816
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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: 100
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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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+ | 1.0105 | 0.99 | 78 | 0.9788 | 0.5251 |
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+ | 0.8749 | 2.0 | 157 | 0.8167 | 0.6323 |
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+ | 0.7352 | 2.99 | 235 | 0.6498 | 0.7179 |
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+ | 0.647 | 4.0 | 314 | 0.5619 | 0.7630 |
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+ | 0.5949 | 4.99 | 392 | 0.5068 | 0.7892 |
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+ | 0.5773 | 6.0 | 471 | 0.4673 | 0.8058 |
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+ | 0.5401 | 6.99 | 549 | 0.4277 | 0.8211 |
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+ | 0.5116 | 8.0 | 628 | 0.4089 | 0.8314 |
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+ | 0.4751 | 8.99 | 706 | 0.3891 | 0.8399 |
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+ | 0.4659 | 10.0 | 785 | 0.3599 | 0.8574 |
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+ | 0.4457 | 10.99 | 863 | 0.3493 | 0.8592 |
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+ | 0.4021 | 12.0 | 942 | 0.3180 | 0.8751 |
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+ | 0.3925 | 12.99 | 1020 | 0.2824 | 0.8863 |
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+ | 0.3737 | 14.0 | 1099 | 0.2686 | 0.8960 |
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+ | 0.3573 | 14.99 | 1177 | 0.2509 | 0.8978 |
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+ | 0.3463 | 16.0 | 1256 | 0.2214 | 0.9101 |
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+ | 0.3174 | 16.99 | 1334 | 0.2233 | 0.9143 |
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+ | 0.3029 | 18.0 | 1413 | 0.2065 | 0.9222 |
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+ | 0.27 | 18.99 | 1491 | 0.1900 | 0.9287 |
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+ | 0.2796 | 20.0 | 1570 | 0.1843 | 0.9294 |
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+ | 0.2649 | 20.99 | 1648 | 0.1811 | 0.9291 |
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+ | 0.2561 | 22.0 | 1727 | 0.1679 | 0.9345 |
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+ | 0.2384 | 22.99 | 1805 | 0.1579 | 0.9401 |
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+ | 0.2415 | 24.0 | 1884 | 0.1414 | 0.9462 |
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+ | 0.2282 | 24.99 | 1962 | 0.1478 | 0.9439 |
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+ | 0.2345 | 26.0 | 2041 | 0.1523 | 0.9419 |
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+ | 0.2196 | 26.99 | 2119 | 0.1240 | 0.9529 |
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+ | 0.2118 | 28.0 | 2198 | 0.1228 | 0.9502 |
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+ | 0.1977 | 28.99 | 2276 | 0.1192 | 0.9565 |
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+ | 0.1994 | 30.0 | 2355 | 0.1208 | 0.9529 |
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+ | 0.1959 | 30.99 | 2433 | 0.1189 | 0.9563 |
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+ | 0.1946 | 32.0 | 2512 | 0.1106 | 0.9617 |
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+ | 0.1777 | 32.99 | 2590 | 0.1048 | 0.9587 |
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+ | 0.1895 | 34.0 | 2669 | 0.0965 | 0.9643 |
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+ | 0.1779 | 34.99 | 2747 | 0.1081 | 0.9623 |
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+ | 0.1717 | 36.0 | 2826 | 0.1155 | 0.9590 |
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+ | 0.1715 | 36.99 | 2904 | 0.1006 | 0.9630 |
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+ | 0.1748 | 38.0 | 2983 | 0.0894 | 0.9695 |
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+ | 0.1677 | 38.99 | 3061 | 0.1037 | 0.9639 |
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+ | 0.1723 | 40.0 | 3140 | 0.1009 | 0.9637 |
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+ | 0.1552 | 40.99 | 3218 | 0.0867 | 0.9697 |
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+ | 0.1658 | 42.0 | 3297 | 0.0860 | 0.9702 |
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+ | 0.1531 | 42.99 | 3375 | 0.0921 | 0.9673 |
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+ | 0.1593 | 44.0 | 3454 | 0.0941 | 0.9646 |
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+ | 0.1568 | 44.99 | 3532 | 0.0879 | 0.9686 |
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+ | 0.1621 | 46.0 | 3611 | 0.0929 | 0.9693 |
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+ | 0.1602 | 46.99 | 3689 | 0.0777 | 0.9744 |
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+ | 0.1397 | 48.0 | 3768 | 0.0821 | 0.9733 |
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+ | 0.1475 | 48.99 | 3846 | 0.0810 | 0.9711 |
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+ | 0.1452 | 50.0 | 3925 | 0.0943 | 0.9697 |
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+ | 0.148 | 50.99 | 4003 | 0.0943 | 0.9704 |
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+ | 0.1392 | 52.0 | 4082 | 0.0869 | 0.9695 |
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+ | 0.1389 | 52.99 | 4160 | 0.0876 | 0.9738 |
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+ | 0.1295 | 54.0 | 4239 | 0.0716 | 0.9749 |
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+ | 0.1394 | 54.99 | 4317 | 0.0760 | 0.9751 |
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+ | 0.1354 | 56.0 | 4396 | 0.0771 | 0.9726 |
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+ | 0.1303 | 56.99 | 4474 | 0.0738 | 0.9738 |
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+ | 0.1274 | 58.0 | 4553 | 0.0834 | 0.9724 |
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+ | 0.1276 | 58.99 | 4631 | 0.0801 | 0.9747 |
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+ | 0.1372 | 60.0 | 4710 | 0.0667 | 0.9762 |
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+ | 0.1417 | 60.99 | 4788 | 0.0747 | 0.9738 |
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+ | 0.1249 | 62.0 | 4867 | 0.0735 | 0.9744 |
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+ | 0.1212 | 62.99 | 4945 | 0.0665 | 0.9780 |
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+ | 0.1218 | 64.0 | 5024 | 0.0680 | 0.9771 |
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+ | 0.1193 | 64.99 | 5102 | 0.0679 | 0.9760 |
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+ | 0.1148 | 66.0 | 5181 | 0.0685 | 0.9774 |
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+ | 0.1242 | 66.99 | 5259 | 0.0647 | 0.9800 |
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+ | 0.1167 | 68.0 | 5338 | 0.0646 | 0.9783 |
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+ | 0.117 | 68.99 | 5416 | 0.0763 | 0.9765 |
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+ | 0.1153 | 70.0 | 5495 | 0.0720 | 0.9753 |
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+ | 0.12 | 70.99 | 5573 | 0.0717 | 0.9771 |
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+ | 0.1054 | 72.0 | 5652 | 0.0677 | 0.9767 |
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+ | 0.1183 | 72.99 | 5730 | 0.0741 | 0.9756 |
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+ | 0.1082 | 74.0 | 5809 | 0.0676 | 0.9787 |
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+ | 0.1088 | 74.99 | 5887 | 0.0700 | 0.9751 |
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+ | 0.1125 | 76.0 | 5966 | 0.0663 | 0.9785 |
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+ | 0.1099 | 76.99 | 6044 | 0.0622 | 0.9789 |
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+ | 0.1128 | 78.0 | 6123 | 0.0660 | 0.9794 |
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+ | 0.1178 | 78.99 | 6201 | 0.0699 | 0.9780 |
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+ | 0.1129 | 80.0 | 6280 | 0.0605 | 0.9794 |
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+ | 0.1013 | 80.99 | 6358 | 0.0694 | 0.9778 |
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+ | 0.1078 | 82.0 | 6437 | 0.0652 | 0.9783 |
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+ | 0.0994 | 82.99 | 6515 | 0.0604 | 0.9812 |
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+ | 0.1093 | 84.0 | 6594 | 0.0600 | 0.9805 |
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+ | 0.1039 | 84.99 | 6672 | 0.0646 | 0.9787 |
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+ | 0.0963 | 86.0 | 6751 | 0.0654 | 0.9789 |
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+ | 0.1053 | 86.99 | 6829 | 0.0627 | 0.9803 |
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+ | 0.0982 | 88.0 | 6908 | 0.0619 | 0.9800 |
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+ | 0.0944 | 88.99 | 6986 | 0.0607 | 0.9796 |
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+ | 0.0959 | 90.0 | 7065 | 0.0661 | 0.9800 |
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+ | 0.101 | 90.99 | 7143 | 0.0642 | 0.9809 |
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+ | 0.1095 | 92.0 | 7222 | 0.0607 | 0.9807 |
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+ | 0.1079 | 92.99 | 7300 | 0.0610 | 0.9803 |
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+ | 0.1153 | 94.0 | 7379 | 0.0632 | 0.9798 |
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+ | 0.1022 | 94.99 | 7457 | 0.0618 | 0.9812 |
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+ | 0.1079 | 96.0 | 7536 | 0.0606 | 0.9809 |
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+ | 0.0942 | 96.99 | 7614 | 0.0612 | 0.9800 |
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+ | 0.0927 | 98.0 | 7693 | 0.0598 | 0.9809 |
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+ | 0.1032 | 98.99 | 7771 | 0.0604 | 0.9814 |
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+ | 0.0925 | 99.36 | 7800 | 0.0603 | 0.9816 |
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  ### Framework versions