swin-tiny-patch4-window7-224-finetuned-eurosat_DATA6_20240305
This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0731
- Accuracy: 0.9747
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.0432 | 0.99 | 78 | 1.0627 | 0.4531 |
0.9424 | 2.0 | 157 | 1.0076 | 0.5103 |
0.8681 | 2.99 | 235 | 0.9070 | 0.5778 |
0.7533 | 4.0 | 314 | 0.7472 | 0.6731 |
0.655 | 4.99 | 392 | 0.6276 | 0.7339 |
0.6233 | 6.0 | 471 | 0.5866 | 0.7572 |
0.592 | 6.99 | 549 | 0.5434 | 0.7711 |
0.5502 | 8.0 | 628 | 0.4688 | 0.8045 |
0.5192 | 8.99 | 706 | 0.4532 | 0.8139 |
0.4923 | 10.0 | 785 | 0.4253 | 0.8303 |
0.487 | 10.99 | 863 | 0.4004 | 0.8365 |
0.432 | 12.0 | 942 | 0.3639 | 0.8531 |
0.4194 | 12.99 | 1020 | 0.3321 | 0.8675 |
0.4031 | 14.0 | 1099 | 0.3658 | 0.8545 |
0.3803 | 14.99 | 1177 | 0.2912 | 0.8861 |
0.3742 | 16.0 | 1256 | 0.2719 | 0.8906 |
0.3767 | 16.99 | 1334 | 0.2803 | 0.8874 |
0.3281 | 18.0 | 1413 | 0.2732 | 0.8975 |
0.313 | 18.99 | 1491 | 0.2466 | 0.9065 |
0.3147 | 20.0 | 1570 | 0.2308 | 0.9096 |
0.3053 | 20.99 | 1648 | 0.2295 | 0.9170 |
0.2784 | 22.0 | 1727 | 0.2241 | 0.9170 |
0.2788 | 22.99 | 1805 | 0.2084 | 0.9240 |
0.2665 | 24.0 | 1884 | 0.2074 | 0.9197 |
0.2528 | 24.99 | 1962 | 0.1779 | 0.9339 |
0.2518 | 26.0 | 2041 | 0.1782 | 0.9359 |
0.2502 | 26.99 | 2119 | 0.1645 | 0.9415 |
0.2387 | 28.0 | 2198 | 0.1856 | 0.9312 |
0.2459 | 28.99 | 2276 | 0.1496 | 0.9430 |
0.2122 | 30.0 | 2355 | 0.1503 | 0.9469 |
0.2139 | 30.99 | 2433 | 0.1598 | 0.9424 |
0.2065 | 32.0 | 2512 | 0.1484 | 0.9448 |
0.2052 | 32.99 | 2590 | 0.1613 | 0.9417 |
0.2104 | 34.0 | 2669 | 0.1353 | 0.9561 |
0.2068 | 34.99 | 2747 | 0.1461 | 0.9464 |
0.2009 | 36.0 | 2826 | 0.1307 | 0.9529 |
0.1862 | 36.99 | 2904 | 0.1375 | 0.9509 |
0.1925 | 38.0 | 2983 | 0.1216 | 0.9556 |
0.192 | 38.99 | 3061 | 0.1215 | 0.9549 |
0.1974 | 40.0 | 3140 | 0.1301 | 0.9587 |
0.1858 | 40.99 | 3218 | 0.1156 | 0.9565 |
0.1802 | 42.0 | 3297 | 0.1105 | 0.9635 |
0.1828 | 42.99 | 3375 | 0.1394 | 0.9565 |
0.1767 | 44.0 | 3454 | 0.1142 | 0.9612 |
0.1852 | 44.99 | 3532 | 0.1097 | 0.9630 |
0.1717 | 46.0 | 3611 | 0.1147 | 0.9596 |
0.1799 | 46.99 | 3689 | 0.1222 | 0.9610 |
0.166 | 48.0 | 3768 | 0.1133 | 0.9608 |
0.1638 | 48.99 | 3846 | 0.1058 | 0.9646 |
0.1524 | 50.0 | 3925 | 0.1045 | 0.9626 |
0.1581 | 50.99 | 4003 | 0.1065 | 0.9632 |
0.1616 | 52.0 | 4082 | 0.0996 | 0.9691 |
0.158 | 52.99 | 4160 | 0.1048 | 0.9648 |
0.1466 | 54.0 | 4239 | 0.1047 | 0.9639 |
0.1617 | 54.99 | 4317 | 0.0982 | 0.9650 |
0.1624 | 56.0 | 4396 | 0.1010 | 0.9643 |
0.1465 | 56.99 | 4474 | 0.0999 | 0.9657 |
0.1368 | 58.0 | 4553 | 0.0977 | 0.9648 |
0.1445 | 58.99 | 4631 | 0.1106 | 0.9630 |
0.1368 | 60.0 | 4710 | 0.0985 | 0.9661 |
0.1401 | 60.99 | 4788 | 0.0913 | 0.9686 |
0.1447 | 62.0 | 4867 | 0.0888 | 0.9688 |
0.1356 | 62.99 | 4945 | 0.0894 | 0.9704 |
0.1358 | 64.0 | 5024 | 0.0851 | 0.9700 |
0.1345 | 64.99 | 5102 | 0.0920 | 0.9709 |
0.1297 | 66.0 | 5181 | 0.0858 | 0.9704 |
0.134 | 66.99 | 5259 | 0.0875 | 0.9709 |
0.1252 | 68.0 | 5338 | 0.0954 | 0.9688 |
0.139 | 68.99 | 5416 | 0.0869 | 0.9733 |
0.1341 | 70.0 | 5495 | 0.0816 | 0.9691 |
0.1263 | 70.99 | 5573 | 0.0835 | 0.9726 |
0.1247 | 72.0 | 5652 | 0.0858 | 0.9686 |
0.122 | 72.99 | 5730 | 0.0891 | 0.9688 |
0.118 | 74.0 | 5809 | 0.0829 | 0.9715 |
0.1236 | 74.99 | 5887 | 0.0743 | 0.9751 |
0.1305 | 76.0 | 5966 | 0.0882 | 0.9715 |
0.1219 | 76.99 | 6044 | 0.0826 | 0.9722 |
0.1184 | 78.0 | 6123 | 0.0838 | 0.9722 |
0.124 | 78.99 | 6201 | 0.0819 | 0.9731 |
0.118 | 80.0 | 6280 | 0.0859 | 0.9709 |
0.1082 | 80.99 | 6358 | 0.0816 | 0.9726 |
0.1195 | 82.0 | 6437 | 0.0783 | 0.9722 |
0.1101 | 82.99 | 6515 | 0.0792 | 0.9747 |
0.1233 | 84.0 | 6594 | 0.0759 | 0.9753 |
0.1181 | 84.99 | 6672 | 0.0783 | 0.9722 |
0.1153 | 86.0 | 6751 | 0.0817 | 0.9715 |
0.1116 | 86.99 | 6829 | 0.0856 | 0.9733 |
0.1066 | 88.0 | 6908 | 0.0788 | 0.9758 |
0.1047 | 88.99 | 6986 | 0.0771 | 0.9735 |
0.1109 | 90.0 | 7065 | 0.0733 | 0.9744 |
0.1125 | 90.99 | 7143 | 0.0748 | 0.9747 |
0.1166 | 92.0 | 7222 | 0.0757 | 0.9738 |
0.1021 | 92.99 | 7300 | 0.0778 | 0.9740 |
0.1089 | 94.0 | 7379 | 0.0741 | 0.9753 |
0.1061 | 94.99 | 7457 | 0.0718 | 0.9765 |
0.1144 | 96.0 | 7536 | 0.0728 | 0.9747 |
0.1146 | 96.99 | 7614 | 0.0737 | 0.9747 |
0.1097 | 98.0 | 7693 | 0.0727 | 0.9749 |
0.1139 | 98.99 | 7771 | 0.0731 | 0.9747 |
0.1025 | 99.36 | 7800 | 0.0731 | 0.9747 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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