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RobertoSonic
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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV21

Image Classification
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swinv2
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  1. README.md +44 -44
  2. model.safetensors +1 -1
  3. runs/Jan21_16-36-23_31423d62c322/events.out.tfevents.1737477399.31423d62c322.1929.0 +2 -2
README.md CHANGED
@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9577
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- - Accuracy: 0.7308
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  ## Model description
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@@ -53,48 +53,48 @@ 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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- | No log | 0.9302 | 10 | 1.6006 | 0.2308 |
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- | 6.7273 | 1.9302 | 20 | 1.4679 | 0.3077 |
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- | 5.9474 | 2.9302 | 30 | 1.1914 | 0.5385 |
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- | 4.3002 | 3.9302 | 40 | 1.1952 | 0.4615 |
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- | 2.9717 | 4.9302 | 50 | 0.9783 | 0.5769 |
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- | 2.4828 | 5.9302 | 60 | 0.9832 | 0.5769 |
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- | 2.4828 | 6.9302 | 70 | 1.0621 | 0.5 |
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- | 2.0276 | 7.9302 | 80 | 0.9041 | 0.6538 |
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- | 1.7041 | 8.9302 | 90 | 0.9577 | 0.7308 |
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- | 1.3967 | 9.9302 | 100 | 1.0290 | 0.6731 |
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- | 1.3079 | 10.9302 | 110 | 0.9864 | 0.7115 |
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- | 1.0486 | 11.9302 | 120 | 1.0006 | 0.6731 |
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- | 1.0486 | 12.9302 | 130 | 0.9503 | 0.6154 |
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- | 1.0704 | 13.9302 | 140 | 0.9435 | 0.7308 |
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- | 0.9929 | 14.9302 | 150 | 1.0601 | 0.6346 |
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- | 0.8911 | 15.9302 | 160 | 1.0282 | 0.6731 |
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- | 0.7702 | 16.9302 | 170 | 1.1858 | 0.6923 |
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- | 0.718 | 17.9302 | 180 | 1.2644 | 0.6538 |
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- | 0.718 | 18.9302 | 190 | 1.1404 | 0.6346 |
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- | 0.6526 | 19.9302 | 200 | 1.2203 | 0.6923 |
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- | 0.6617 | 20.9302 | 210 | 1.3465 | 0.6731 |
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- | 0.5699 | 21.9302 | 220 | 1.3863 | 0.6538 |
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- | 0.5228 | 22.9302 | 230 | 1.2727 | 0.7115 |
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- | 0.4321 | 23.9302 | 240 | 1.1947 | 0.7115 |
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- | 0.4321 | 24.9302 | 250 | 1.3973 | 0.6346 |
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- | 0.557 | 25.9302 | 260 | 1.4322 | 0.6538 |
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- | 0.4569 | 26.9302 | 270 | 1.3256 | 0.6538 |
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- | 0.4395 | 27.9302 | 280 | 1.3908 | 0.6731 |
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- | 0.5587 | 28.9302 | 290 | 1.5056 | 0.6923 |
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- | 0.439 | 29.9302 | 300 | 1.3811 | 0.6538 |
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- | 0.439 | 30.9302 | 310 | 1.3694 | 0.6154 |
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- | 0.3989 | 31.9302 | 320 | 1.4759 | 0.6538 |
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- | 0.312 | 32.9302 | 330 | 1.5514 | 0.6346 |
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- | 0.3728 | 33.9302 | 340 | 1.5129 | 0.6538 |
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- | 0.4031 | 34.9302 | 350 | 1.4914 | 0.6538 |
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- | 0.3523 | 35.9302 | 360 | 1.5589 | 0.6731 |
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- | 0.3523 | 36.9302 | 370 | 1.5689 | 0.6731 |
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- | 0.265 | 37.9302 | 380 | 1.6179 | 0.6731 |
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- | 0.3189 | 38.9302 | 390 | 1.5963 | 0.6538 |
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- | 0.2976 | 39.9302 | 400 | 1.5738 | 0.6346 |
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- | 0.3097 | 40.9302 | 410 | 1.5787 | 0.6346 |
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- | 0.269 | 41.9302 | 420 | 1.5819 | 0.6346 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2461
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+ - Accuracy: 0.6923
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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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+ | No log | 0.9302 | 10 | 1.5510 | 0.3462 |
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+ | 6.5929 | 1.9302 | 20 | 1.4802 | 0.2692 |
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+ | 5.6252 | 2.9302 | 30 | 1.1115 | 0.4038 |
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+ | 3.874 | 3.9302 | 40 | 0.9996 | 0.5577 |
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+ | 2.7168 | 4.9302 | 50 | 0.8436 | 0.6538 |
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+ | 2.2435 | 5.9302 | 60 | 0.9320 | 0.6154 |
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+ | 2.2435 | 6.9302 | 70 | 0.8412 | 0.6346 |
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+ | 1.9334 | 7.9302 | 80 | 0.8622 | 0.6731 |
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+ | 1.6303 | 8.9302 | 90 | 0.9152 | 0.7115 |
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+ | 1.2748 | 9.9302 | 100 | 0.9721 | 0.6731 |
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+ | 1.0945 | 10.9302 | 110 | 1.0827 | 0.6538 |
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+ | 0.8395 | 11.9302 | 120 | 0.9153 | 0.7115 |
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+ | 0.8395 | 12.9302 | 130 | 0.8631 | 0.7308 |
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+ | 0.8587 | 13.9302 | 140 | 1.1039 | 0.6538 |
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+ | 0.8574 | 14.9302 | 150 | 1.0463 | 0.6923 |
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+ | 0.7096 | 15.9302 | 160 | 0.9991 | 0.7115 |
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+ | 0.6606 | 16.9302 | 170 | 1.0519 | 0.6731 |
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+ | 0.5513 | 17.9302 | 180 | 1.0865 | 0.7115 |
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+ | 0.5513 | 18.9302 | 190 | 1.1140 | 0.6731 |
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+ | 0.61 | 19.9302 | 200 | 1.0290 | 0.6731 |
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+ | 0.5278 | 20.9302 | 210 | 1.1003 | 0.6923 |
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+ | 0.4639 | 21.9302 | 220 | 1.2472 | 0.6538 |
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+ | 0.4719 | 22.9302 | 230 | 1.1546 | 0.6923 |
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+ | 0.4212 | 23.9302 | 240 | 1.1084 | 0.7308 |
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+ | 0.4212 | 24.9302 | 250 | 1.2953 | 0.6731 |
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+ | 0.4109 | 25.9302 | 260 | 1.1868 | 0.7308 |
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+ | 0.4236 | 26.9302 | 270 | 1.2560 | 0.6346 |
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+ | 0.3638 | 27.9302 | 280 | 1.2161 | 0.7115 |
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+ | 0.3944 | 28.9302 | 290 | 1.1582 | 0.7308 |
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+ | 0.3621 | 29.9302 | 300 | 1.2993 | 0.6923 |
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+ | 0.3621 | 30.9302 | 310 | 1.1401 | 0.7115 |
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+ | 0.3203 | 31.9302 | 320 | 1.3228 | 0.7115 |
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+ | 0.3014 | 32.9302 | 330 | 1.2813 | 0.6923 |
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+ | 0.3464 | 33.9302 | 340 | 1.4768 | 0.6538 |
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+ | 0.2891 | 34.9302 | 350 | 1.2304 | 0.7308 |
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+ | 0.3153 | 35.9302 | 360 | 1.3096 | 0.6923 |
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+ | 0.3153 | 36.9302 | 370 | 1.3565 | 0.7115 |
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+ | 0.2762 | 37.9302 | 380 | 1.2931 | 0.6923 |
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+ | 0.3191 | 38.9302 | 390 | 1.2441 | 0.7308 |
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+ | 0.3009 | 39.9302 | 400 | 1.2110 | 0.7308 |
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+ | 0.2645 | 40.9302 | 410 | 1.2433 | 0.7115 |
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+ | 0.2497 | 41.9302 | 420 | 1.2461 | 0.6923 |
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  ### Framework versions
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