--- license: apache-2.0 base_model: nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-wsdmhar 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: 0.9683195592286501 --- # swin-tiny-patch4-window7-224-finetuned-wsdmhar This model is a fine-tuned version of [nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat](https://huggingface.co./nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1444 - Accuracy: 0.9683 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0624 | 1.0 | 53 | 0.8879 | 0.6092 | | 0.6893 | 2.0 | 106 | 0.6601 | 0.7090 | | 0.6152 | 3.0 | 159 | 0.5114 | 0.7855 | | 0.5456 | 4.0 | 212 | 0.3819 | 0.8423 | | 0.4673 | 5.0 | 265 | 0.3267 | 0.8719 | | 0.4166 | 6.0 | 318 | 0.2804 | 0.9039 | | 0.3757 | 7.0 | 371 | 0.2881 | 0.8994 | | 0.3798 | 8.0 | 424 | 0.2635 | 0.9032 | | 0.3303 | 9.0 | 477 | 0.2703 | 0.9074 | | 0.3346 | 10.0 | 530 | 0.2565 | 0.9005 | | 0.2971 | 11.0 | 583 | 0.2182 | 0.9311 | | 0.2992 | 12.0 | 636 | 0.2240 | 0.9256 | | 0.2637 | 13.0 | 689 | 0.2131 | 0.9239 | | 0.2653 | 14.0 | 742 | 0.1801 | 0.9397 | | 0.2472 | 15.0 | 795 | 0.1807 | 0.9377 | | 0.2263 | 16.0 | 848 | 0.1612 | 0.9466 | | 0.1786 | 17.0 | 901 | 0.1735 | 0.9418 | | 0.2103 | 18.0 | 954 | 0.1786 | 0.9463 | | 0.1725 | 19.0 | 1007 | 0.1631 | 0.9473 | | 0.1787 | 20.0 | 1060 | 0.1439 | 0.9532 | | 0.1924 | 21.0 | 1113 | 0.1388 | 0.9504 | | 0.1662 | 22.0 | 1166 | 0.1470 | 0.9508 | | 0.1724 | 23.0 | 1219 | 0.1538 | 0.9497 | | 0.1633 | 24.0 | 1272 | 0.1731 | 0.9384 | | 0.174 | 25.0 | 1325 | 0.1555 | 0.9539 | | 0.1657 | 26.0 | 1378 | 0.1542 | 0.9494 | | 0.1513 | 27.0 | 1431 | 0.1526 | 0.9508 | | 0.126 | 28.0 | 1484 | 0.1560 | 0.9511 | | 0.1508 | 29.0 | 1537 | 0.1607 | 0.9480 | | 0.1368 | 30.0 | 1590 | 0.1729 | 0.9435 | | 0.1166 | 31.0 | 1643 | 0.1555 | 0.9532 | | 0.1076 | 32.0 | 1696 | 0.1400 | 0.9580 | | 0.1189 | 33.0 | 1749 | 0.1419 | 0.9590 | | 0.1512 | 34.0 | 1802 | 0.1364 | 0.9580 | | 0.1323 | 35.0 | 1855 | 0.1497 | 0.9539 | | 0.1031 | 36.0 | 1908 | 0.1437 | 0.9580 | | 0.1215 | 37.0 | 1961 | 0.1460 | 0.9559 | | 0.1069 | 38.0 | 2014 | 0.1362 | 0.9601 | | 0.129 | 39.0 | 2067 | 0.1490 | 0.9590 | | 0.1202 | 40.0 | 2120 | 0.1616 | 0.9545 | | 0.1011 | 41.0 | 2173 | 0.1518 | 0.9570 | | 0.1092 | 42.0 | 2226 | 0.1308 | 0.9618 | | 0.1163 | 43.0 | 2279 | 0.1458 | 0.9590 | | 0.1074 | 44.0 | 2332 | 0.1414 | 0.9549 | | 0.0814 | 45.0 | 2385 | 0.1509 | 0.9580 | | 0.0985 | 46.0 | 2438 | 0.1287 | 0.9628 | | 0.0863 | 47.0 | 2491 | 0.1277 | 0.9625 | | 0.0932 | 48.0 | 2544 | 0.1453 | 0.9559 | | 0.0863 | 49.0 | 2597 | 0.1520 | 0.9566 | | 0.0887 | 50.0 | 2650 | 0.1279 | 0.9656 | | 0.0744 | 51.0 | 2703 | 0.1552 | 0.9566 | | 0.0928 | 52.0 | 2756 | 0.1465 | 0.9621 | | 0.0776 | 53.0 | 2809 | 0.1575 | 0.9583 | | 0.088 | 54.0 | 2862 | 0.1614 | 0.9563 | | 0.0909 | 55.0 | 2915 | 0.1312 | 0.9638 | | 0.089 | 56.0 | 2968 | 0.1357 | 0.9652 | | 0.0587 | 57.0 | 3021 | 0.1510 | 0.9614 | | 0.0931 | 58.0 | 3074 | 0.1466 | 0.9580 | | 0.0878 | 59.0 | 3127 | 0.1499 | 0.9590 | | 0.0725 | 60.0 | 3180 | 0.1524 | 0.9597 | | 0.0543 | 61.0 | 3233 | 0.1543 | 0.9583 | | 0.0773 | 62.0 | 3286 | 0.1513 | 0.9635 | | 0.0626 | 63.0 | 3339 | 0.1511 | 0.9601 | | 0.0649 | 64.0 | 3392 | 0.1467 | 0.9594 | | 0.0705 | 65.0 | 3445 | 0.1443 | 0.9590 | | 0.0737 | 66.0 | 3498 | 0.1361 | 0.9607 | | 0.0518 | 67.0 | 3551 | 0.1441 | 0.9594 | | 0.0502 | 68.0 | 3604 | 0.1535 | 0.9590 | | 0.0701 | 69.0 | 3657 | 0.1362 | 0.9663 | | 0.0826 | 70.0 | 3710 | 0.1492 | 0.9611 | | 0.0715 | 71.0 | 3763 | 0.1615 | 0.9625 | | 0.0635 | 72.0 | 3816 | 0.1488 | 0.9642 | | 0.0522 | 73.0 | 3869 | 0.1456 | 0.9621 | | 0.0485 | 74.0 | 3922 | 0.1386 | 0.9645 | | 0.0629 | 75.0 | 3975 | 0.1463 | 0.9632 | | 0.0568 | 76.0 | 4028 | 0.1472 | 0.9621 | | 0.0556 | 77.0 | 4081 | 0.1440 | 0.9659 | | 0.0547 | 78.0 | 4134 | 0.1421 | 0.9635 | | 0.0527 | 79.0 | 4187 | 0.1444 | 0.9683 | | 0.054 | 80.0 | 4240 | 0.1464 | 0.9628 | | 0.0641 | 81.0 | 4293 | 0.1491 | 0.9635 | | 0.0546 | 82.0 | 4346 | 0.1529 | 0.9611 | | 0.059 | 83.0 | 4399 | 0.1462 | 0.9652 | | 0.0485 | 84.0 | 4452 | 0.1567 | 0.9632 | | 0.0388 | 85.0 | 4505 | 0.1548 | 0.9621 | | 0.0421 | 86.0 | 4558 | 0.1484 | 0.9621 | | 0.0375 | 87.0 | 4611 | 0.1681 | 0.9597 | | 0.0376 | 88.0 | 4664 | 0.1513 | 0.9632 | | 0.0514 | 89.0 | 4717 | 0.1485 | 0.9642 | | 0.0598 | 90.0 | 4770 | 0.1541 | 0.9638 | | 0.0431 | 91.0 | 4823 | 0.1474 | 0.9628 | | 0.0432 | 92.0 | 4876 | 0.1498 | 0.9645 | | 0.0391 | 93.0 | 4929 | 0.1506 | 0.9645 | | 0.0408 | 94.0 | 4982 | 0.1462 | 0.9642 | | 0.0335 | 95.0 | 5035 | 0.1509 | 0.9652 | | 0.0447 | 96.0 | 5088 | 0.1508 | 0.9635 | | 0.0477 | 97.0 | 5141 | 0.1510 | 0.9635 | | 0.0504 | 98.0 | 5194 | 0.1510 | 0.9642 | | 0.0406 | 99.0 | 5247 | 0.1479 | 0.9649 | | 0.0343 | 100.0 | 5300 | 0.1480 | 0.9645 | ### Framework versions - Transformers 4.43.2 - Pytorch 2.3.1+cu118 - Datasets 2.20.0 - Tokenizers 0.19.1