--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-st-ucihar 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: 0.8363553943789664 --- # swin-tiny-patch4-window7-224-finetuned-st-ucihar This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7803 - Accuracy: 0.8364 ## 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: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:--------:|:----:|:---------------:|:--------:| | 1.6826 | 0.9938 | 40 | 1.6510 | 0.3504 | | 1.3306 | 1.9876 | 80 | 1.2070 | 0.4710 | | 1.0249 | 2.9814 | 120 | 0.9149 | 0.5512 | | 0.8922 | 4.0 | 161 | 0.7313 | 0.6587 | | 0.791 | 4.9938 | 201 | 0.6777 | 0.6881 | | 0.7267 | 5.9876 | 241 | 0.6398 | 0.7026 | | 0.7106 | 6.9814 | 281 | 0.6299 | 0.7035 | | 0.7038 | 8.0 | 322 | 0.6062 | 0.7058 | | 0.6694 | 8.9938 | 362 | 0.5736 | 0.7226 | | 0.6489 | 9.9876 | 402 | 0.5858 | 0.7248 | | 0.6587 | 10.9814 | 442 | 0.5437 | 0.7434 | | 0.6376 | 12.0 | 483 | 0.5259 | 0.7520 | | 0.6494 | 12.9938 | 523 | 0.5807 | 0.7307 | | 0.6236 | 13.9876 | 563 | 0.5236 | 0.7430 | | 0.5931 | 14.9814 | 603 | 0.5463 | 0.7384 | | 0.605 | 16.0 | 644 | 0.5360 | 0.7371 | | 0.5422 | 16.9938 | 684 | 0.4954 | 0.7788 | | 0.5485 | 17.9876 | 724 | 0.4910 | 0.7756 | | 0.5586 | 18.9814 | 764 | 0.5052 | 0.7738 | | 0.6011 | 20.0 | 805 | 0.5036 | 0.7679 | | 0.5726 | 20.9938 | 845 | 0.5308 | 0.7588 | | 0.514 | 21.9876 | 885 | 0.5064 | 0.7720 | | 0.5431 | 22.9814 | 925 | 0.4680 | 0.7892 | | 0.5348 | 24.0 | 966 | 0.6083 | 0.7430 | | 0.532 | 24.9938 | 1006 | 0.4889 | 0.7815 | | 0.5263 | 25.9876 | 1046 | 0.5268 | 0.7634 | | 0.497 | 26.9814 | 1086 | 0.5057 | 0.7779 | | 0.4929 | 28.0 | 1127 | 0.5560 | 0.7570 | | 0.4828 | 28.9938 | 1167 | 0.4701 | 0.7928 | | 0.4604 | 29.9876 | 1207 | 0.4656 | 0.8001 | | 0.4472 | 30.9814 | 1247 | 0.4619 | 0.8083 | | 0.455 | 32.0 | 1288 | 0.4920 | 0.7969 | | 0.4467 | 32.9938 | 1328 | 0.4698 | 0.7987 | | 0.4327 | 33.9876 | 1368 | 0.4489 | 0.8055 | | 0.3918 | 34.9814 | 1408 | 0.5249 | 0.7906 | | 0.4222 | 36.0 | 1449 | 0.4380 | 0.8069 | | 0.3864 | 36.9938 | 1489 | 0.4695 | 0.8128 | | 0.3865 | 37.9876 | 1529 | 0.5208 | 0.7811 | | 0.3759 | 38.9814 | 1569 | 0.4972 | 0.8019 | | 0.375 | 40.0 | 1610 | 0.4667 | 0.8105 | | 0.4016 | 40.9938 | 1650 | 0.4632 | 0.8178 | | 0.3788 | 41.9876 | 1690 | 0.4670 | 0.8078 | | 0.3517 | 42.9814 | 1730 | 0.4745 | 0.8101 | | 0.3707 | 44.0 | 1771 | 0.4526 | 0.8187 | | 0.3422 | 44.9938 | 1811 | 0.4893 | 0.8005 | | 0.3661 | 45.9876 | 1851 | 0.4748 | 0.8087 | | 0.3522 | 46.9814 | 1891 | 0.4923 | 0.7992 | | 0.385 | 48.0 | 1932 | 0.4335 | 0.8273 | | 0.306 | 48.9938 | 1972 | 0.4823 | 0.8228 | | 0.3163 | 49.9876 | 2012 | 0.5157 | 0.8069 | | 0.3199 | 50.9814 | 2052 | 0.5283 | 0.8015 | | 0.3248 | 52.0 | 2093 | 0.4652 | 0.8191 | | 0.2969 | 52.9938 | 2133 | 0.5260 | 0.8024 | | 0.3138 | 53.9876 | 2173 | 0.4862 | 0.8069 | | 0.2665 | 54.9814 | 2213 | 0.4781 | 0.8250 | | 0.2932 | 56.0 | 2254 | 0.5094 | 0.8214 | | 0.2819 | 56.9938 | 2294 | 0.4921 | 0.8178 | | 0.2677 | 57.9876 | 2334 | 0.5372 | 0.8087 | | 0.2623 | 58.9814 | 2374 | 0.4847 | 0.8286 | | 0.2584 | 60.0 | 2415 | 0.5754 | 0.8069 | | 0.2637 | 60.9938 | 2455 | 0.5297 | 0.8182 | | 0.2391 | 61.9876 | 2495 | 0.5187 | 0.8214 | | 0.2426 | 62.9814 | 2535 | 0.5719 | 0.8137 | | 0.2405 | 64.0 | 2576 | 0.5118 | 0.8232 | | 0.2132 | 64.9938 | 2616 | 0.5691 | 0.8123 | | 0.2572 | 65.9876 | 2656 | 0.5452 | 0.8209 | | 0.2255 | 66.9814 | 2696 | 0.5650 | 0.8073 | | 0.2614 | 68.0 | 2737 | 0.5387 | 0.8214 | | 0.2284 | 68.9938 | 2777 | 0.6056 | 0.8141 | | 0.2371 | 69.9876 | 2817 | 0.5906 | 0.8128 | | 0.2089 | 70.9814 | 2857 | 0.5550 | 0.8119 | | 0.2276 | 72.0 | 2898 | 0.5511 | 0.8214 | | 0.2192 | 72.9938 | 2938 | 0.6162 | 0.8259 | | 0.2076 | 73.9876 | 2978 | 0.5663 | 0.8237 | | 0.1938 | 74.9814 | 3018 | 0.6118 | 0.8191 | | 0.2274 | 76.0 | 3059 | 0.5603 | 0.8268 | | 0.2271 | 76.9938 | 3099 | 0.6312 | 0.8128 | | 0.2023 | 77.9876 | 3139 | 0.6300 | 0.8123 | | 0.1792 | 78.9814 | 3179 | 0.5776 | 0.8268 | | 0.1796 | 80.0 | 3220 | 0.6266 | 0.8209 | | 0.1994 | 80.9938 | 3260 | 0.5468 | 0.8228 | | 0.1857 | 81.9876 | 3300 | 0.6080 | 0.8205 | | 0.1636 | 82.9814 | 3340 | 0.7066 | 0.8160 | | 0.1665 | 84.0 | 3381 | 0.6064 | 0.8277 | | 0.183 | 84.9938 | 3421 | 0.6019 | 0.8273 | | 0.1761 | 85.9876 | 3461 | 0.6420 | 0.8196 | | 0.1673 | 86.9814 | 3501 | 0.6287 | 0.8255 | | 0.1946 | 88.0 | 3542 | 0.6024 | 0.8228 | | 0.1511 | 88.9938 | 3582 | 0.6774 | 0.8169 | | 0.1828 | 89.9876 | 3622 | 0.6015 | 0.8255 | | 0.1758 | 90.9814 | 3662 | 0.5969 | 0.8300 | | 0.1797 | 92.0 | 3703 | 0.6464 | 0.8200 | | 0.176 | 92.9938 | 3743 | 0.6287 | 0.8173 | | 0.1616 | 93.9876 | 3783 | 0.6914 | 0.8209 | | 0.1783 | 94.9814 | 3823 | 0.6511 | 0.8218 | | 0.1492 | 96.0 | 3864 | 0.6382 | 0.8264 | | 0.1578 | 96.9938 | 3904 | 0.6391 | 0.8241 | | 0.1574 | 97.9876 | 3944 | 0.6505 | 0.8255 | | 0.1556 | 98.9814 | 3984 | 0.6302 | 0.8241 | | 0.1396 | 100.0 | 4025 | 0.6634 | 0.8155 | | 0.1246 | 100.9938 | 4065 | 0.6633 | 0.8264 | | 0.1592 | 101.9876 | 4105 | 0.6815 | 0.8160 | | 0.1393 | 102.9814 | 4145 | 0.6418 | 0.8237 | | 0.1722 | 104.0 | 4186 | 0.6322 | 0.8318 | | 0.1499 | 104.9938 | 4226 | 0.6901 | 0.8196 | | 0.1282 | 105.9876 | 4266 | 0.6544 | 0.8309 | | 0.1428 | 106.9814 | 4306 | 0.6581 | 0.8291 | | 0.1478 | 108.0 | 4347 | 0.6825 | 0.8291 | | 0.1453 | 108.9938 | 4387 | 0.6873 | 0.8237 | | 0.1216 | 109.9876 | 4427 | 0.7075 | 0.8223 | | 0.1449 | 110.9814 | 4467 | 0.6929 | 0.8232 | | 0.137 | 112.0 | 4508 | 0.7139 | 0.8205 | | 0.1177 | 112.9938 | 4548 | 0.6981 | 0.8305 | | 0.1005 | 113.9876 | 4588 | 0.6840 | 0.8205 | | 0.1305 | 114.9814 | 4628 | 0.6747 | 0.8273 | | 0.1192 | 116.0 | 4669 | 0.6886 | 0.8259 | | 0.1067 | 116.9938 | 4709 | 0.6612 | 0.8209 | | 0.1122 | 117.9876 | 4749 | 0.6500 | 0.8259 | | 0.1295 | 118.9814 | 4789 | 0.6948 | 0.8232 | | 0.1304 | 120.0 | 4830 | 0.6651 | 0.8309 | | 0.1334 | 120.9938 | 4870 | 0.7304 | 0.8187 | | 0.1104 | 121.9876 | 4910 | 0.7365 | 0.8205 | | 0.1132 | 122.9814 | 4950 | 0.7270 | 0.8300 | | 0.1115 | 124.0 | 4991 | 0.7062 | 0.8228 | | 0.1079 | 124.9938 | 5031 | 0.7579 | 0.8268 | | 0.1192 | 125.9876 | 5071 | 0.7321 | 0.8205 | | 0.0994 | 126.9814 | 5111 | 0.7219 | 0.8291 | | 0.111 | 128.0 | 5152 | 0.7064 | 0.8273 | | 0.1089 | 128.9938 | 5192 | 0.7056 | 0.8282 | | 0.1062 | 129.9876 | 5232 | 0.6814 | 0.8323 | | 0.1046 | 130.9814 | 5272 | 0.6843 | 0.8309 | | 0.1013 | 132.0 | 5313 | 0.6807 | 0.8327 | | 0.0879 | 132.9938 | 5353 | 0.7080 | 0.8336 | | 0.1114 | 133.9876 | 5393 | 0.7129 | 0.8241 | | 0.1133 | 134.9814 | 5433 | 0.7376 | 0.8264 | | 0.1067 | 136.0 | 5474 | 0.7579 | 0.8259 | | 0.1104 | 136.9938 | 5514 | 0.7178 | 0.8291 | | 0.0893 | 137.9876 | 5554 | 0.7315 | 0.8300 | | 0.1074 | 138.9814 | 5594 | 0.7312 | 0.8318 | | 0.0983 | 140.0 | 5635 | 0.7362 | 0.8286 | | 0.1093 | 140.9938 | 5675 | 0.7493 | 0.8286 | | 0.1166 | 141.9876 | 5715 | 0.7205 | 0.8286 | | 0.0969 | 142.9814 | 5755 | 0.7494 | 0.8291 | | 0.1174 | 144.0 | 5796 | 0.6960 | 0.8336 | | 0.1044 | 144.9938 | 5836 | 0.7111 | 0.8282 | | 0.0866 | 145.9876 | 5876 | 0.7152 | 0.8364 | | 0.092 | 146.9814 | 5916 | 0.7078 | 0.8327 | | 0.0883 | 148.0 | 5957 | 0.7182 | 0.8341 | | 0.0824 | 148.9938 | 5997 | 0.7095 | 0.8359 | | 0.0953 | 149.9876 | 6037 | 0.7324 | 0.8354 | | 0.0896 | 150.9814 | 6077 | 0.7032 | 0.8400 | | 0.1025 | 152.0 | 6118 | 0.6938 | 0.8323 | | 0.0966 | 152.9938 | 6158 | 0.6991 | 0.8404 | | 0.0891 | 153.9876 | 6198 | 0.7346 | 0.8354 | | 0.0733 | 154.9814 | 6238 | 0.7340 | 0.8350 | | 0.0944 | 156.0 | 6279 | 0.7525 | 0.8277 | | 0.0934 | 156.9938 | 6319 | 0.7683 | 0.8305 | | 0.0768 | 157.9876 | 6359 | 0.7692 | 0.8286 | | 0.0918 | 158.9814 | 6399 | 0.7387 | 0.8413 | | 0.0886 | 160.0 | 6440 | 0.7705 | 0.8327 | | 0.0836 | 160.9938 | 6480 | 0.7491 | 0.8327 | | 0.0968 | 161.9876 | 6520 | 0.7663 | 0.8246 | | 0.0748 | 162.9814 | 6560 | 0.7460 | 0.8305 | | 0.0696 | 164.0 | 6601 | 0.7491 | 0.8332 | | 0.0853 | 164.9938 | 6641 | 0.7788 | 0.8327 | | 0.0726 | 165.9876 | 6681 | 0.7440 | 0.8382 | | 0.0715 | 166.9814 | 6721 | 0.7518 | 0.8373 | | 0.0699 | 168.0 | 6762 | 0.7574 | 0.8354 | | 0.0749 | 168.9938 | 6802 | 0.7564 | 0.8323 | | 0.0842 | 169.9876 | 6842 | 0.7829 | 0.8286 | | 0.0822 | 170.9814 | 6882 | 0.7753 | 0.8327 | | 0.0807 | 172.0 | 6923 | 0.7611 | 0.8359 | | 0.0752 | 172.9938 | 6963 | 0.7673 | 0.8345 | | 0.075 | 173.9876 | 7003 | 0.7815 | 0.8364 | | 0.0845 | 174.9814 | 7043 | 0.7745 | 0.8382 | | 0.0827 | 176.0 | 7084 | 0.7683 | 0.8373 | | 0.0883 | 176.9938 | 7124 | 0.7842 | 0.8327 | | 0.0774 | 177.9876 | 7164 | 0.7736 | 0.8368 | | 0.0817 | 178.9814 | 7204 | 0.7852 | 0.8341 | | 0.0804 | 180.0 | 7245 | 0.7686 | 0.8314 | | 0.0671 | 180.9938 | 7285 | 0.7767 | 0.8359 | | 0.076 | 181.9876 | 7325 | 0.7715 | 0.8350 | | 0.0572 | 182.9814 | 7365 | 0.7740 | 0.8286 | | 0.0823 | 184.0 | 7406 | 0.7757 | 0.8341 | | 0.0662 | 184.9938 | 7446 | 0.7720 | 0.8336 | | 0.0805 | 185.9876 | 7486 | 0.7696 | 0.8368 | | 0.0763 | 186.9814 | 7526 | 0.7768 | 0.8377 | | 0.0711 | 188.0 | 7567 | 0.7720 | 0.8350 | | 0.0576 | 188.9938 | 7607 | 0.7845 | 0.8314 | | 0.0667 | 189.9876 | 7647 | 0.7749 | 0.8336 | | 0.0631 | 190.9814 | 7687 | 0.7774 | 0.8350 | | 0.0744 | 192.0 | 7728 | 0.7778 | 0.8327 | | 0.0672 | 192.9938 | 7768 | 0.7862 | 0.8323 | | 0.0738 | 193.9876 | 7808 | 0.7843 | 0.8345 | | 0.0754 | 194.9814 | 7848 | 0.7850 | 0.8368 | | 0.0887 | 196.0 | 7889 | 0.7835 | 0.8364 | | 0.0898 | 196.9938 | 7929 | 0.7810 | 0.8373 | | 0.0543 | 197.9876 | 7969 | 0.7801 | 0.8364 | | 0.0605 | 198.7578 | 8000 | 0.7803 | 0.8364 | ### Framework versions - Transformers 4.44.0 - Pytorch 1.12.1+cu113 - Datasets 2.21.0 - Tokenizers 0.19.1