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

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  1. README.md +51 -51
  2. model.safetensors +1 -1
  3. runs/Feb22_05-41-08_72d11355c0d9/events.out.tfevents.1708580476.72d11355c0d9.616.0 +2 -2
README.md CHANGED
@@ -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: 0.9503105590062112
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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: 0.1879
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- - Accuracy: 0.9503
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  ## Model description
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@@ -67,54 +67,54 @@ 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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- | 1.6144 | 0.96 | 11 | 1.0071 | 0.8447 |
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- | 0.8116 | 2.0 | 23 | 0.5227 | 0.8571 |
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- | 0.6078 | 2.96 | 34 | 0.4213 | 0.8571 |
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- | 0.5151 | 4.0 | 46 | 0.3357 | 0.8758 |
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- | 0.4499 | 4.96 | 57 | 0.3467 | 0.9068 |
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- | 0.4254 | 6.0 | 69 | 0.2344 | 0.9193 |
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- | 0.3266 | 6.96 | 80 | 0.2107 | 0.9379 |
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- | 0.3018 | 8.0 | 92 | 0.1818 | 0.9379 |
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- | 0.3339 | 8.96 | 103 | 0.1928 | 0.9379 |
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- | 0.2594 | 10.0 | 115 | 0.1936 | 0.9317 |
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- | 0.2476 | 10.96 | 126 | 0.1543 | 0.9317 |
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- | 0.2294 | 12.0 | 138 | 0.1827 | 0.9441 |
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- | 0.2193 | 12.96 | 149 | 0.1676 | 0.9317 |
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- | 0.1924 | 14.0 | 161 | 0.1553 | 0.9379 |
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- | 0.2148 | 14.96 | 172 | 0.1387 | 0.9379 |
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- | 0.1674 | 16.0 | 184 | 0.1449 | 0.9379 |
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- | 0.1815 | 16.96 | 195 | 0.1833 | 0.9317 |
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- | 0.1861 | 18.0 | 207 | 0.1818 | 0.9441 |
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- | 0.1629 | 18.96 | 218 | 0.2484 | 0.9255 |
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- | 0.1609 | 20.0 | 230 | 0.1661 | 0.9503 |
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- | 0.132 | 20.96 | 241 | 0.1538 | 0.9441 |
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- | 0.1468 | 22.0 | 253 | 0.1597 | 0.9565 |
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- | 0.0926 | 22.96 | 264 | 0.1613 | 0.9565 |
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- | 0.102 | 24.0 | 276 | 0.1420 | 0.9441 |
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- | 0.1178 | 24.96 | 287 | 0.1429 | 0.9441 |
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- | 0.1311 | 26.0 | 299 | 0.1832 | 0.9503 |
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- | 0.0982 | 26.96 | 310 | 0.2140 | 0.9441 |
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- | 0.0865 | 28.0 | 322 | 0.2040 | 0.9565 |
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- | 0.0919 | 28.96 | 333 | 0.1878 | 0.9503 |
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- | 0.085 | 30.0 | 345 | 0.1935 | 0.9565 |
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- | 0.0918 | 30.96 | 356 | 0.1787 | 0.9503 |
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- | 0.0939 | 32.0 | 368 | 0.1932 | 0.9441 |
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- | 0.1236 | 32.96 | 379 | 0.1736 | 0.9379 |
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- | 0.0819 | 34.0 | 391 | 0.1798 | 0.9503 |
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- | 0.0906 | 34.96 | 402 | 0.1937 | 0.9379 |
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- | 0.0865 | 36.0 | 414 | 0.1809 | 0.9379 |
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- | 0.0709 | 36.96 | 425 | 0.2062 | 0.9379 |
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- | 0.0781 | 38.0 | 437 | 0.1749 | 0.9503 |
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- | 0.0772 | 38.96 | 448 | 0.2176 | 0.9441 |
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- | 0.0535 | 40.0 | 460 | 0.2164 | 0.9503 |
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- | 0.0608 | 40.96 | 471 | 0.1976 | 0.9503 |
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- | 0.072 | 42.0 | 483 | 0.1837 | 0.9441 |
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- | 0.0657 | 42.96 | 494 | 0.2000 | 0.9565 |
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- | 0.0824 | 44.0 | 506 | 0.1865 | 0.9503 |
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- | 0.0584 | 44.96 | 517 | 0.1870 | 0.9565 |
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- | 0.0556 | 46.0 | 529 | 0.1863 | 0.9503 |
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- | 0.0516 | 46.96 | 540 | 0.1894 | 0.9503 |
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- | 0.06 | 47.83 | 550 | 0.1879 | 0.9503 |
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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: 0.9440993788819876
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  ---
27
 
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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: 0.3046
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+ - Accuracy: 0.9441
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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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+ | 1.2705 | 0.96 | 11 | 0.8209 | 0.7826 |
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+ | 0.7129 | 2.0 | 23 | 0.6566 | 0.7826 |
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+ | 0.5478 | 2.96 | 34 | 0.5473 | 0.7950 |
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+ | 0.4453 | 4.0 | 46 | 0.4564 | 0.8385 |
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+ | 0.4091 | 4.96 | 57 | 0.4124 | 0.8571 |
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+ | 0.2986 | 6.0 | 69 | 0.3571 | 0.8882 |
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+ | 0.2763 | 6.96 | 80 | 0.3426 | 0.8944 |
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+ | 0.2486 | 8.0 | 92 | 0.3065 | 0.8944 |
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+ | 0.24 | 8.96 | 103 | 0.3175 | 0.9130 |
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+ | 0.1894 | 10.0 | 115 | 0.2984 | 0.9006 |
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+ | 0.2043 | 10.96 | 126 | 0.3646 | 0.9130 |
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+ | 0.2029 | 12.0 | 138 | 0.3181 | 0.9130 |
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+ | 0.1813 | 12.96 | 149 | 0.3068 | 0.9006 |
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+ | 0.1615 | 14.0 | 161 | 0.2755 | 0.9068 |
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+ | 0.1563 | 14.96 | 172 | 0.3337 | 0.8944 |
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+ | 0.1415 | 16.0 | 184 | 0.3257 | 0.8944 |
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+ | 0.1585 | 16.96 | 195 | 0.2778 | 0.9068 |
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+ | 0.1257 | 18.0 | 207 | 0.2788 | 0.9068 |
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+ | 0.1231 | 18.96 | 218 | 0.2897 | 0.9068 |
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+ | 0.1435 | 20.0 | 230 | 0.2904 | 0.9130 |
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+ | 0.1223 | 20.96 | 241 | 0.2618 | 0.9193 |
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+ | 0.1173 | 22.0 | 253 | 0.2867 | 0.9255 |
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+ | 0.1203 | 22.96 | 264 | 0.2705 | 0.9317 |
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+ | 0.1074 | 24.0 | 276 | 0.3051 | 0.9255 |
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+ | 0.0846 | 24.96 | 287 | 0.2954 | 0.9130 |
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+ | 0.0988 | 26.0 | 299 | 0.3130 | 0.8820 |
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+ | 0.0889 | 26.96 | 310 | 0.2721 | 0.9006 |
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+ | 0.0797 | 28.0 | 322 | 0.2896 | 0.9068 |
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+ | 0.0924 | 28.96 | 333 | 0.3321 | 0.9006 |
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+ | 0.0774 | 30.0 | 345 | 0.3164 | 0.9317 |
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+ | 0.0714 | 30.96 | 356 | 0.3089 | 0.9193 |
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+ | 0.0483 | 32.0 | 368 | 0.3255 | 0.9193 |
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+ | 0.0689 | 32.96 | 379 | 0.3083 | 0.9255 |
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+ | 0.0739 | 34.0 | 391 | 0.2874 | 0.9317 |
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+ | 0.0579 | 34.96 | 402 | 0.2909 | 0.9255 |
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+ | 0.0724 | 36.0 | 414 | 0.3062 | 0.9068 |
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+ | 0.0345 | 36.96 | 425 | 0.3139 | 0.9255 |
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+ | 0.068 | 38.0 | 437 | 0.3392 | 0.9193 |
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+ | 0.0749 | 38.96 | 448 | 0.2638 | 0.9379 |
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+ | 0.0573 | 40.0 | 460 | 0.3032 | 0.9130 |
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+ | 0.0537 | 40.96 | 471 | 0.2750 | 0.9441 |
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+ | 0.0588 | 42.0 | 483 | 0.2674 | 0.9379 |
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+ | 0.0462 | 42.96 | 494 | 0.2948 | 0.9379 |
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+ | 0.0547 | 44.0 | 506 | 0.3136 | 0.9441 |
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+ | 0.0437 | 44.96 | 517 | 0.2951 | 0.9441 |
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+ | 0.051 | 46.0 | 529 | 0.2961 | 0.9441 |
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+ | 0.0418 | 46.96 | 540 | 0.3055 | 0.9441 |
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+ | 0.0653 | 47.83 | 550 | 0.3046 | 0.9441 |
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  ### Framework versions
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