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swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3209
- Accuracy: 0.8902
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 |
|---|---|---|---|---|
| No log | 1.0 | 8 | 2.7448 | 0.0314 |
| 2.7716 | 2.0 | 16 | 2.5834 | 0.1765 |
| 2.5974 | 3.0 | 24 | 2.3608 | 0.3020 |
| 2.3426 | 4.0 | 32 | 2.1157 | 0.3333 |
| 1.9747 | 5.0 | 40 | 1.7539 | 0.4627 |
| 1.9747 | 6.0 | 48 | 1.3641 | 0.6078 |
| 1.5182 | 7.0 | 56 | 1.0755 | 0.6471 |
| 1.198 | 8.0 | 64 | 0.8743 | 0.7216 |
| 1.0206 | 9.0 | 72 | 0.7666 | 0.7294 |
| 0.8731 | 10.0 | 80 | 0.7035 | 0.7490 |
| 0.8731 | 11.0 | 88 | 0.6122 | 0.7608 |
| 0.7938 | 12.0 | 96 | 0.6508 | 0.7490 |
| 0.7286 | 13.0 | 104 | 0.5081 | 0.7961 |
| 0.659 | 14.0 | 112 | 0.5536 | 0.7961 |
| 0.6232 | 15.0 | 120 | 0.5079 | 0.8 |
| 0.6232 | 16.0 | 128 | 0.4483 | 0.8314 |
| 0.6028 | 17.0 | 136 | 0.4096 | 0.8157 |
| 0.5333 | 18.0 | 144 | 0.3710 | 0.8510 |
| 0.5053 | 19.0 | 152 | 0.4810 | 0.8039 |
| 0.4717 | 20.0 | 160 | 0.4121 | 0.8235 |
| 0.4717 | 21.0 | 168 | 0.4021 | 0.8392 |
| 0.4728 | 22.0 | 176 | 0.3780 | 0.8588 |
| 0.4347 | 23.0 | 184 | 0.3374 | 0.8745 |
| 0.4545 | 24.0 | 192 | 0.4056 | 0.8431 |
| 0.3954 | 25.0 | 200 | 0.4088 | 0.8745 |
| 0.3954 | 26.0 | 208 | 0.4169 | 0.8392 |
| 0.4145 | 27.0 | 216 | 0.3262 | 0.8706 |
| 0.3895 | 28.0 | 224 | 0.4235 | 0.8706 |
| 0.4185 | 29.0 | 232 | 0.3482 | 0.8706 |
| 0.3686 | 30.0 | 240 | 0.3088 | 0.8824 |
| 0.3686 | 31.0 | 248 | 0.3230 | 0.8902 |
| 0.3617 | 32.0 | 256 | 0.3473 | 0.8824 |
| 0.3136 | 33.0 | 264 | 0.3793 | 0.8627 |
| 0.3482 | 34.0 | 272 | 0.3477 | 0.8588 |
| 0.3519 | 35.0 | 280 | 0.3692 | 0.8667 |
| 0.3519 | 36.0 | 288 | 0.3611 | 0.8627 |
| 0.3311 | 37.0 | 296 | 0.3233 | 0.8745 |
| 0.3222 | 38.0 | 304 | 0.3416 | 0.8627 |
| 0.3013 | 39.0 | 312 | 0.3198 | 0.8824 |
| 0.2871 | 40.0 | 320 | 0.3308 | 0.8667 |
| 0.2871 | 41.0 | 328 | 0.3246 | 0.8667 |
| 0.3154 | 42.0 | 336 | 0.3943 | 0.8667 |
| 0.2735 | 43.0 | 344 | 0.3186 | 0.8784 |
| 0.2911 | 44.0 | 352 | 0.3132 | 0.8824 |
| 0.266 | 45.0 | 360 | 0.3204 | 0.8980 |
| 0.266 | 46.0 | 368 | 0.3097 | 0.8784 |
| 0.2686 | 47.0 | 376 | 0.3075 | 0.8902 |
| 0.2818 | 48.0 | 384 | 0.3192 | 0.8902 |
| 0.2492 | 49.0 | 392 | 0.3434 | 0.8745 |
| 0.276 | 50.0 | 400 | 0.3237 | 0.8824 |
| 0.276 | 51.0 | 408 | 0.3450 | 0.8745 |
| 0.245 | 52.0 | 416 | 0.3284 | 0.8706 |
| 0.2292 | 53.0 | 424 | 0.3263 | 0.8902 |
| 0.2252 | 54.0 | 432 | 0.3216 | 0.8745 |
| 0.2483 | 55.0 | 440 | 0.3359 | 0.8863 |
| 0.2483 | 56.0 | 448 | 0.3314 | 0.8902 |
| 0.2549 | 57.0 | 456 | 0.3932 | 0.8745 |
| 0.2247 | 58.0 | 464 | 0.3189 | 0.8745 |
| 0.2344 | 59.0 | 472 | 0.3251 | 0.8745 |
| 0.2315 | 60.0 | 480 | 0.3289 | 0.8824 |
| 0.2315 | 61.0 | 488 | 0.3058 | 0.8745 |
| 0.2109 | 62.0 | 496 | 0.2999 | 0.8863 |
| 0.2325 | 63.0 | 504 | 0.3078 | 0.8980 |
| 0.2126 | 64.0 | 512 | 0.3531 | 0.8784 |
| 0.1975 | 65.0 | 520 | 0.3394 | 0.8902 |
| 0.1975 | 66.0 | 528 | 0.3113 | 0.8902 |
| 0.1998 | 67.0 | 536 | 0.3365 | 0.8941 |
| 0.2208 | 68.0 | 544 | 0.2854 | 0.9020 |
| 0.2126 | 69.0 | 552 | 0.3170 | 0.8941 |
| 0.2352 | 70.0 | 560 | 0.3155 | 0.8824 |
| 0.2352 | 71.0 | 568 | 0.3327 | 0.8824 |
| 0.1724 | 72.0 | 576 | 0.3503 | 0.8902 |
| 0.2038 | 73.0 | 584 | 0.3309 | 0.8824 |
| 0.1919 | 74.0 | 592 | 0.3299 | 0.8902 |
| 0.2199 | 75.0 | 600 | 0.3347 | 0.8863 |
| 0.2199 | 76.0 | 608 | 0.3471 | 0.8824 |
| 0.2075 | 77.0 | 616 | 0.3437 | 0.8863 |
| 0.2206 | 78.0 | 624 | 0.3161 | 0.8824 |
| 0.1655 | 79.0 | 632 | 0.3227 | 0.8784 |
| 0.1765 | 80.0 | 640 | 0.3302 | 0.8784 |
| 0.1765 | 81.0 | 648 | 0.3153 | 0.8745 |
| 0.1832 | 82.0 | 656 | 0.3010 | 0.8745 |
| 0.185 | 83.0 | 664 | 0.3266 | 0.8941 |
| 0.1627 | 84.0 | 672 | 0.3192 | 0.8941 |
| 0.176 | 85.0 | 680 | 0.3125 | 0.8863 |
| 0.176 | 86.0 | 688 | 0.3241 | 0.8745 |
| 0.1723 | 87.0 | 696 | 0.3124 | 0.8784 |
| 0.1477 | 88.0 | 704 | 0.3109 | 0.8745 |
| 0.1703 | 89.0 | 712 | 0.3196 | 0.8824 |
| 0.1919 | 90.0 | 720 | 0.3186 | 0.8980 |
| 0.1919 | 91.0 | 728 | 0.3178 | 0.8902 |
| 0.1465 | 92.0 | 736 | 0.3241 | 0.8824 |
| 0.155 | 93.0 | 744 | 0.3281 | 0.8784 |
| 0.1829 | 94.0 | 752 | 0.3263 | 0.8824 |
| 0.167 | 95.0 | 760 | 0.3282 | 0.8824 |
| 0.167 | 96.0 | 768 | 0.3290 | 0.8824 |
| 0.166 | 97.0 | 776 | 0.3253 | 0.8902 |
| 0.1756 | 98.0 | 784 | 0.3231 | 0.8863 |
| 0.157 | 99.0 | 792 | 0.3215 | 0.8902 |
| 0.1492 | 100.0 | 800 | 0.3209 | 0.8902 |
Framework versions
- Transformers 4.33.3
- Pytorch 1.11.0+cu113
- Datasets 2.14.5
- Tokenizers 0.13.3
