--- tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat_DATA7_20240411 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9784449576597383 --- # swin-tiny-patch4-window7-224-finetuned-eurosat_DATA7_20240411 This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0669 - Accuracy: 0.9784 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.9517 | 1.0 | 365 | 0.9502 | 0.5231 | | 0.7427 | 2.0 | 731 | 0.7194 | 0.6734 | | 0.6384 | 3.0 | 1096 | 0.6085 | 0.7290 | | 0.6007 | 4.0 | 1462 | 0.5564 | 0.7512 | | 0.606 | 5.0 | 1827 | 0.5444 | 0.7606 | | 0.5432 | 6.0 | 2193 | 0.4737 | 0.7991 | | 0.5239 | 7.0 | 2558 | 0.4484 | 0.8048 | | 0.4525 | 8.0 | 2924 | 0.3606 | 0.8487 | | 0.4318 | 9.0 | 3289 | 0.3628 | 0.8514 | | 0.4213 | 10.0 | 3655 | 0.2967 | 0.8759 | | 0.3846 | 11.0 | 4020 | 0.2828 | 0.8880 | | 0.3833 | 12.0 | 4386 | 0.2494 | 0.9011 | | 0.3671 | 13.0 | 4751 | 0.2430 | 0.9028 | | 0.3458 | 14.0 | 5117 | 0.2222 | 0.9107 | | 0.3049 | 15.0 | 5482 | 0.2041 | 0.9190 | | 0.3198 | 16.0 | 5848 | 0.1761 | 0.9338 | | 0.2648 | 17.0 | 6213 | 0.1772 | 0.9348 | | 0.2517 | 18.0 | 6579 | 0.1846 | 0.9303 | | 0.251 | 19.0 | 6944 | 0.1848 | 0.9299 | | 0.2351 | 20.0 | 7310 | 0.1448 | 0.9448 | | 0.2604 | 21.0 | 7675 | 0.1467 | 0.9425 | | 0.245 | 22.0 | 8041 | 0.1292 | 0.9503 | | 0.2095 | 23.0 | 8406 | 0.1381 | 0.9490 | | 0.2022 | 24.0 | 8772 | 0.1542 | 0.9469 | | 0.2041 | 25.0 | 9137 | 0.1229 | 0.9563 | | 0.1899 | 26.0 | 9503 | 0.1225 | 0.9557 | | 0.167 | 27.0 | 9868 | 0.1137 | 0.9588 | | 0.2101 | 28.0 | 10234 | 0.0978 | 0.9646 | | 0.2187 | 29.0 | 10599 | 0.1138 | 0.9588 | | 0.1875 | 30.0 | 10965 | 0.1256 | 0.9517 | | 0.1807 | 31.0 | 11330 | 0.1043 | 0.9625 | | 0.1874 | 32.0 | 11696 | 0.1147 | 0.9613 | | 0.1863 | 33.0 | 12061 | 0.1035 | 0.9627 | | 0.1607 | 34.0 | 12427 | 0.0920 | 0.9652 | | 0.1597 | 35.0 | 12792 | 0.1021 | 0.9617 | | 0.1643 | 36.0 | 13158 | 0.1007 | 0.9638 | | 0.1858 | 37.0 | 13523 | 0.0987 | 0.9667 | | 0.152 | 38.0 | 13889 | 0.1038 | 0.9652 | | 0.1622 | 39.0 | 14254 | 0.0964 | 0.9665 | | 0.1598 | 40.0 | 14620 | 0.0976 | 0.9679 | | 0.1438 | 41.0 | 14985 | 0.0918 | 0.9675 | | 0.1355 | 42.0 | 15351 | 0.0884 | 0.9694 | | 0.1563 | 43.0 | 15716 | 0.0905 | 0.9679 | | 0.1319 | 44.0 | 16082 | 0.1046 | 0.9675 | | 0.1613 | 45.0 | 16447 | 0.0792 | 0.9711 | | 0.1454 | 46.0 | 16813 | 0.0872 | 0.9690 | | 0.1166 | 47.0 | 17178 | 0.0857 | 0.9700 | | 0.1297 | 48.0 | 17544 | 0.0779 | 0.9734 | | 0.1328 | 49.0 | 17909 | 0.0792 | 0.9738 | | 0.1212 | 50.0 | 18275 | 0.0736 | 0.9731 | | 0.1275 | 51.0 | 18640 | 0.0900 | 0.9684 | | 0.0988 | 52.0 | 19006 | 0.0739 | 0.9732 | | 0.126 | 53.0 | 19371 | 0.0854 | 0.9721 | | 0.138 | 54.0 | 19737 | 0.0858 | 0.9715 | | 0.1169 | 55.0 | 20102 | 0.0770 | 0.9740 | | 0.1328 | 56.0 | 20468 | 0.0780 | 0.9759 | | 0.1231 | 57.0 | 20833 | 0.0770 | 0.9734 | | 0.128 | 58.0 | 21199 | 0.0692 | 0.9738 | | 0.1233 | 59.0 | 21564 | 0.0763 | 0.9721 | | 0.1131 | 60.0 | 21930 | 0.0695 | 0.9754 | | 0.1032 | 61.0 | 22295 | 0.0795 | 0.9731 | | 0.146 | 62.0 | 22661 | 0.0702 | 0.9752 | | 0.1117 | 63.0 | 23026 | 0.0725 | 0.9754 | | 0.1137 | 64.0 | 23392 | 0.0710 | 0.9767 | | 0.1083 | 65.0 | 23757 | 0.0764 | 0.9738 | | 0.1053 | 66.0 | 24123 | 0.0712 | 0.9732 | | 0.1193 | 67.0 | 24488 | 0.0813 | 0.9729 | | 0.0901 | 68.0 | 24854 | 0.0758 | 0.9752 | | 0.1142 | 69.0 | 25219 | 0.0672 | 0.9758 | | 0.1166 | 70.0 | 25585 | 0.0803 | 0.9754 | | 0.0835 | 71.0 | 25950 | 0.0791 | 0.9727 | | 0.0989 | 72.0 | 26316 | 0.0801 | 0.9731 | | 0.0952 | 73.0 | 26681 | 0.0717 | 0.9754 | | 0.0945 | 74.0 | 27047 | 0.0666 | 0.9761 | | 0.0944 | 75.0 | 27412 | 0.0703 | 0.9752 | | 0.1092 | 76.0 | 27778 | 0.0624 | 0.9773 | | 0.0976 | 77.0 | 28143 | 0.0637 | 0.9773 | | 0.1008 | 78.0 | 28509 | 0.0744 | 0.9744 | | 0.0755 | 79.0 | 28874 | 0.0694 | 0.9767 | | 0.1023 | 80.0 | 29240 | 0.0800 | 0.9752 | | 0.0987 | 81.0 | 29605 | 0.0773 | 0.9744 | | 0.0806 | 82.0 | 29971 | 0.0689 | 0.9773 | | 0.1019 | 83.0 | 30336 | 0.0749 | 0.9767 | | 0.0994 | 84.0 | 30702 | 0.0696 | 0.9765 | | 0.0897 | 85.0 | 31067 | 0.0698 | 0.9781 | | 0.0636 | 86.0 | 31433 | 0.0672 | 0.9775 | | 0.098 | 87.0 | 31798 | 0.0756 | 0.9767 | | 0.0955 | 88.0 | 32164 | 0.0719 | 0.9771 | | 0.0786 | 89.0 | 32529 | 0.0726 | 0.9771 | | 0.0821 | 90.0 | 32895 | 0.0696 | 0.9769 | | 0.0844 | 91.0 | 33260 | 0.0691 | 0.9771 | | 0.0926 | 92.0 | 33626 | 0.0676 | 0.9767 | | 0.0826 | 93.0 | 33991 | 0.0705 | 0.9769 | | 0.0923 | 94.0 | 34357 | 0.0686 | 0.9771 | | 0.0637 | 95.0 | 34722 | 0.0672 | 0.9781 | | 0.0953 | 96.0 | 35088 | 0.0661 | 0.9786 | | 0.0922 | 97.0 | 35453 | 0.0701 | 0.9777 | | 0.0766 | 98.0 | 35819 | 0.0676 | 0.9779 | | 0.0782 | 99.0 | 36184 | 0.0656 | 0.9783 | | 0.0845 | 99.86 | 36500 | 0.0669 | 0.9784 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2