diff --git a/Image/DenseNet/code/backdoor_train.log b/Image/DenseNet/code/backdoor_train.log new file mode 100644 index 0000000000000000000000000000000000000000..f7991486b94a9d031868b5edec3622b92029e9e2 --- /dev/null +++ b/Image/DenseNet/code/backdoor_train.log @@ -0,0 +1,253 @@ +2025-03-14 16:01:30,357 - train - INFO - 开始训练 densenet +2025-03-14 16:01:30,357 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3 +2025-03-14 16:01:35,120 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.303 | Acc: 10.94% +2025-03-14 16:01:41,623 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.009 | Acc: 25.39% +2025-03-14 16:01:48,359 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.872 | Acc: 29.96% +2025-03-14 16:01:54,814 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.780 | Acc: 33.57% +2025-03-14 16:02:02,833 - train - INFO - Epoch: 1 | Test Loss: 1.342 | Test Acc: 52.66% +2025-03-14 16:02:03,082 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.477 | Acc: 41.41% +2025-03-14 16:02:09,563 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.335 | Acc: 51.83% +2025-03-14 16:02:16,183 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.242 | Acc: 55.57% +2025-03-14 16:02:22,685 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.173 | Acc: 58.28% +2025-03-14 16:02:30,658 - train - INFO - Epoch: 2 | Test Loss: 1.093 | Test Acc: 61.78% +2025-03-14 16:02:41,822 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.999 | Acc: 63.28% +2025-03-14 16:02:48,275 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.908 | Acc: 68.81% +2025-03-14 16:02:55,542 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.893 | Acc: 69.34% +2025-03-14 16:03:02,152 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.874 | Acc: 69.98% +2025-03-14 16:03:09,933 - train - INFO - Epoch: 3 | Test Loss: 0.895 | Test Acc: 69.23% +2025-03-14 16:03:10,171 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.741 | Acc: 75.00% +2025-03-14 16:03:16,709 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.748 | Acc: 74.24% +2025-03-14 16:03:23,165 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.743 | Acc: 74.83% +2025-03-14 16:03:29,519 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.735 | Acc: 75.22% +2025-03-14 16:03:37,232 - train - INFO - Epoch: 4 | Test Loss: 0.914 | Test Acc: 69.83% +2025-03-14 16:03:48,484 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.680 | Acc: 75.78% +2025-03-14 16:03:55,252 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.674 | Acc: 77.53% +2025-03-14 16:04:01,807 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.667 | Acc: 77.67% +2025-03-14 16:04:08,493 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.661 | Acc: 77.80% +2025-03-14 16:04:16,124 - train - INFO - Epoch: 5 | Test Loss: 0.713 | Test Acc: 76.28% +2025-03-14 16:04:16,366 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.490 | Acc: 87.50% +2025-03-14 16:04:22,967 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.614 | Acc: 79.94% +2025-03-14 16:04:29,555 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.626 | Acc: 79.32% +2025-03-14 16:04:35,837 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.619 | Acc: 79.52% +2025-03-14 16:04:43,506 - train - INFO - Epoch: 6 | Test Loss: 0.767 | Test Acc: 74.45% +2025-03-14 16:04:55,346 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.728 | Acc: 75.00% +2025-03-14 16:05:02,115 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.594 | Acc: 80.22% +2025-03-14 16:05:08,580 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.593 | Acc: 80.24% +2025-03-14 16:05:15,893 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.597 | Acc: 80.01% +2025-03-14 16:05:24,379 - train - INFO - Epoch: 7 | Test Loss: 0.659 | Test Acc: 76.67% +2025-03-14 16:05:24,687 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.511 | Acc: 82.81% +2025-03-14 16:05:31,960 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.554 | Acc: 81.19% +2025-03-14 16:05:38,472 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.564 | Acc: 81.15% +2025-03-14 16:05:45,042 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.568 | Acc: 81.13% +2025-03-14 16:05:53,242 - train - INFO - Epoch: 8 | Test Loss: 0.714 | Test Acc: 76.71% +2025-03-14 16:06:05,059 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.489 | Acc: 85.16% +2025-03-14 16:06:11,780 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.565 | Acc: 81.15% +2025-03-14 16:06:18,537 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.564 | Acc: 81.30% +2025-03-14 16:06:25,070 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.566 | Acc: 81.23% +2025-03-14 16:06:32,812 - train - INFO - Epoch: 9 | Test Loss: 1.349 | Test Acc: 60.93% +2025-03-14 16:06:33,056 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.427 | Acc: 85.94% +2025-03-14 16:06:40,104 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.541 | Acc: 82.09% +2025-03-14 16:06:47,429 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.546 | Acc: 81.77% +2025-03-14 16:06:55,098 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.547 | Acc: 81.80% +2025-03-14 16:07:03,407 - train - INFO - Epoch: 10 | Test Loss: 0.619 | Test Acc: 80.03% +2025-03-14 16:07:15,781 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.509 | Acc: 85.94% +2025-03-14 16:07:22,836 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.546 | Acc: 82.31% +2025-03-14 16:07:29,380 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.547 | Acc: 82.02% +2025-03-14 16:07:36,091 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.545 | Acc: 82.09% +2025-03-14 16:07:44,313 - train - INFO - Epoch: 11 | Test Loss: 0.782 | Test Acc: 74.83% +2025-03-14 16:07:44,642 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.424 | Acc: 88.28% +2025-03-14 16:07:52,452 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.532 | Acc: 82.69% +2025-03-14 16:07:59,454 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.535 | Acc: 82.39% +2025-03-14 16:08:05,751 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.539 | Acc: 82.22% +2025-03-14 16:08:13,415 - train - INFO - Epoch: 12 | Test Loss: 0.667 | Test Acc: 77.20% +2025-03-14 16:08:24,850 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.513 | Acc: 81.25% +2025-03-14 16:08:31,529 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.525 | Acc: 82.91% +2025-03-14 16:08:37,988 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.524 | Acc: 82.91% +2025-03-14 16:08:44,426 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.531 | Acc: 82.63% +2025-03-14 16:08:52,236 - train - INFO - Epoch: 13 | Test Loss: 0.671 | Test Acc: 77.66% +2025-03-14 16:08:52,474 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.400 | Acc: 87.50% +2025-03-14 16:08:58,885 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.513 | Acc: 83.14% +2025-03-14 16:09:05,594 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.526 | Acc: 82.57% +2025-03-14 16:09:13,190 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.525 | Acc: 82.58% +2025-03-14 16:09:21,359 - train - INFO - Epoch: 14 | Test Loss: 0.644 | Test Acc: 78.23% +2025-03-14 16:09:32,590 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.637 | Acc: 78.91% +2025-03-14 16:09:39,160 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.521 | Acc: 82.97% +2025-03-14 16:09:45,611 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.523 | Acc: 82.81% +2025-03-14 16:09:52,133 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.522 | Acc: 82.83% +2025-03-14 16:09:59,547 - train - INFO - Epoch: 15 | Test Loss: 0.614 | Test Acc: 78.16% +2025-03-14 16:09:59,772 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.607 | Acc: 76.56% +2025-03-14 16:10:06,285 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.508 | Acc: 83.32% +2025-03-14 16:10:12,668 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.504 | Acc: 83.33% +2025-03-14 16:10:19,419 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.514 | Acc: 83.00% +2025-03-14 16:10:27,539 - train - INFO - Epoch: 16 | Test Loss: 0.622 | Test Acc: 79.45% +2025-03-14 16:10:38,671 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.503 | Acc: 86.72% +2025-03-14 16:10:45,253 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.503 | Acc: 83.30% +2025-03-14 16:10:51,781 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.507 | Acc: 83.28% +2025-03-14 16:10:58,057 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.504 | Acc: 83.31% +2025-03-14 16:11:05,776 - train - INFO - Epoch: 17 | Test Loss: 0.701 | Test Acc: 77.26% +2025-03-14 16:11:06,018 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.620 | Acc: 78.91% +2025-03-14 16:11:13,323 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.498 | Acc: 83.79% +2025-03-14 16:11:20,126 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.494 | Acc: 83.98% +2025-03-14 16:11:26,804 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.499 | Acc: 83.79% +2025-03-14 16:11:34,825 - train - INFO - Epoch: 18 | Test Loss: 0.751 | Test Acc: 76.20% +2025-03-14 16:11:46,371 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.408 | Acc: 86.72% +2025-03-14 16:11:53,283 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.485 | Acc: 84.00% +2025-03-14 16:12:00,455 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.493 | Acc: 83.82% +2025-03-14 16:12:06,896 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.499 | Acc: 83.56% +2025-03-14 16:12:15,134 - train - INFO - Epoch: 19 | Test Loss: 0.663 | Test Acc: 76.95% +2025-03-14 16:12:15,418 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.390 | Acc: 85.94% +2025-03-14 16:12:21,907 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.500 | Acc: 83.41% +2025-03-14 16:12:28,443 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.498 | Acc: 83.41% +2025-03-14 16:12:35,175 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.494 | Acc: 83.63% +2025-03-14 16:12:45,840 - train - INFO - Epoch: 20 | Test Loss: 0.561 | Test Acc: 81.65% +2025-03-14 16:12:57,454 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.349 | Acc: 89.84% +2025-03-14 16:13:04,132 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.477 | Acc: 83.99% +2025-03-14 16:13:10,608 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.477 | Acc: 84.11% +2025-03-14 16:13:17,405 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.481 | Acc: 84.17% +2025-03-14 16:13:25,297 - train - INFO - Epoch: 21 | Test Loss: 0.593 | Test Acc: 80.85% +2025-03-14 16:13:25,591 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.453 | Acc: 82.81% +2025-03-14 16:13:32,327 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.466 | Acc: 84.52% +2025-03-14 16:13:39,185 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.471 | Acc: 84.26% +2025-03-14 16:13:45,668 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.478 | Acc: 84.05% +2025-03-14 16:13:53,441 - train - INFO - Epoch: 22 | Test Loss: 0.665 | Test Acc: 78.56% +2025-03-14 16:14:05,354 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.474 | Acc: 84.38% +2025-03-14 16:14:11,690 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.474 | Acc: 84.71% +2025-03-14 16:14:18,410 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.482 | Acc: 84.29% +2025-03-14 16:14:24,963 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.478 | Acc: 84.44% +2025-03-14 16:14:32,640 - train - INFO - Epoch: 23 | Test Loss: 1.058 | Test Acc: 68.37% +2025-03-14 16:14:32,934 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.378 | Acc: 89.06% +2025-03-14 16:14:39,551 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.477 | Acc: 84.41% +2025-03-14 16:14:46,168 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.481 | Acc: 84.37% +2025-03-14 16:14:52,732 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.479 | Acc: 84.30% +2025-03-14 16:15:00,623 - train - INFO - Epoch: 24 | Test Loss: 0.636 | Test Acc: 80.17% +2025-03-14 16:15:11,674 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.529 | Acc: 82.03% +2025-03-14 16:15:18,136 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.454 | Acc: 85.12% +2025-03-14 16:15:24,586 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.464 | Acc: 84.64% +2025-03-14 16:15:31,108 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.466 | Acc: 84.54% +2025-03-14 16:15:39,073 - train - INFO - Epoch: 25 | Test Loss: 0.572 | Test Acc: 81.28% +2025-03-14 16:15:39,328 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.496 | Acc: 82.03% +2025-03-14 16:15:45,847 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.468 | Acc: 84.57% +2025-03-14 16:15:52,738 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.469 | Acc: 84.65% +2025-03-14 16:15:59,415 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.466 | Acc: 84.73% +2025-03-14 16:16:06,894 - train - INFO - Epoch: 26 | Test Loss: 0.816 | Test Acc: 74.91% +2025-03-14 16:16:18,304 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.400 | Acc: 85.94% +2025-03-14 16:16:24,917 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.469 | Acc: 84.55% +2025-03-14 16:16:31,318 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.464 | Acc: 84.77% +2025-03-14 16:16:37,744 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.462 | Acc: 84.86% +2025-03-14 16:16:45,639 - train - INFO - Epoch: 27 | Test Loss: 0.561 | Test Acc: 80.96% +2025-03-14 16:16:45,909 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.450 | Acc: 82.81% +2025-03-14 16:16:52,939 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.464 | Acc: 84.66% +2025-03-14 16:16:59,702 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.466 | Acc: 84.61% +2025-03-14 16:17:06,087 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.467 | Acc: 84.57% +2025-03-14 16:17:13,719 - train - INFO - Epoch: 28 | Test Loss: 0.692 | Test Acc: 78.32% +2025-03-14 16:17:25,054 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.428 | Acc: 83.59% +2025-03-14 16:17:31,720 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.457 | Acc: 84.75% +2025-03-14 16:17:38,193 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.463 | Acc: 84.71% +2025-03-14 16:17:44,756 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.465 | Acc: 84.69% +2025-03-14 16:17:52,619 - train - INFO - Epoch: 29 | Test Loss: 0.517 | Test Acc: 82.95% +2025-03-14 16:17:52,871 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.412 | Acc: 86.72% +2025-03-14 16:17:59,452 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.442 | Acc: 85.97% +2025-03-14 16:18:06,228 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.452 | Acc: 85.31% +2025-03-14 16:18:12,888 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.457 | Acc: 85.07% +2025-03-14 16:18:21,005 - train - INFO - Epoch: 30 | Test Loss: 0.656 | Test Acc: 78.35% +2025-03-14 16:18:33,022 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.579 | Acc: 82.81% +2025-03-14 16:18:39,739 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.443 | Acc: 85.60% +2025-03-14 16:18:46,476 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.452 | Acc: 85.29% +2025-03-14 16:18:52,888 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.458 | Acc: 85.02% +2025-03-14 16:19:00,889 - train - INFO - Epoch: 31 | Test Loss: 0.530 | Test Acc: 82.67% +2025-03-14 16:19:01,161 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.321 | Acc: 89.06% +2025-03-14 16:19:07,656 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.446 | Acc: 85.07% +2025-03-14 16:19:14,096 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.451 | Acc: 84.76% +2025-03-14 16:19:20,849 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.454 | Acc: 84.86% +2025-03-14 16:19:28,416 - train - INFO - Epoch: 32 | Test Loss: 0.834 | Test Acc: 73.96% +2025-03-14 16:19:40,125 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.497 | Acc: 82.03% +2025-03-14 16:19:47,217 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.447 | Acc: 85.14% +2025-03-14 16:19:53,869 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.460 | Acc: 84.78% +2025-03-14 16:20:00,742 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.451 | Acc: 85.11% +2025-03-14 16:20:09,000 - train - INFO - Epoch: 33 | Test Loss: 0.449 | Test Acc: 84.73% +2025-03-14 16:20:09,277 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.413 | Acc: 82.03% +2025-03-14 16:20:16,108 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.448 | Acc: 85.24% +2025-03-14 16:20:22,620 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.445 | Acc: 85.32% +2025-03-14 16:20:29,138 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.443 | Acc: 85.38% +2025-03-14 16:20:37,243 - train - INFO - Epoch: 34 | Test Loss: 0.531 | Test Acc: 82.30% +2025-03-14 16:20:49,210 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.354 | Acc: 87.50% +2025-03-14 16:20:55,814 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.432 | Acc: 85.93% +2025-03-14 16:21:02,450 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.439 | Acc: 85.55% +2025-03-14 16:21:08,928 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.442 | Acc: 85.40% +2025-03-14 16:21:17,192 - train - INFO - Epoch: 35 | Test Loss: 0.597 | Test Acc: 80.39% +2025-03-14 16:21:17,446 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.304 | Acc: 89.84% +2025-03-14 16:21:24,132 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.440 | Acc: 85.47% +2025-03-14 16:21:30,524 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.436 | Acc: 85.62% +2025-03-14 16:21:36,979 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.441 | Acc: 85.50% +2025-03-14 16:21:44,631 - train - INFO - Epoch: 36 | Test Loss: 0.642 | Test Acc: 78.95% +2025-03-14 16:21:56,350 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.366 | Acc: 86.72% +2025-03-14 16:22:02,994 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.435 | Acc: 85.64% +2025-03-14 16:22:09,586 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.440 | Acc: 85.30% +2025-03-14 16:22:16,323 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.444 | Acc: 85.23% +2025-03-14 16:22:24,686 - train - INFO - Epoch: 37 | Test Loss: 0.630 | Test Acc: 79.88% +2025-03-14 16:22:24,928 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.366 | Acc: 89.06% +2025-03-14 16:22:31,317 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.420 | Acc: 85.93% +2025-03-14 16:22:37,751 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.421 | Acc: 86.11% +2025-03-14 16:22:44,033 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.427 | Acc: 85.95% +2025-03-14 16:22:52,202 - train - INFO - Epoch: 38 | Test Loss: 0.530 | Test Acc: 82.57% +2025-03-14 16:23:03,115 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.402 | Acc: 85.94% +2025-03-14 16:23:09,617 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.437 | Acc: 85.83% +2025-03-14 16:23:16,188 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.434 | Acc: 85.81% +2025-03-14 16:23:22,787 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.440 | Acc: 85.63% +2025-03-14 16:23:30,722 - train - INFO - Epoch: 39 | Test Loss: 0.605 | Test Acc: 80.62% +2025-03-14 16:23:30,947 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.363 | Acc: 89.84% +2025-03-14 16:23:37,305 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.437 | Acc: 85.76% +2025-03-14 16:23:43,539 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.436 | Acc: 85.80% +2025-03-14 16:23:49,649 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.438 | Acc: 85.76% +2025-03-14 16:23:57,791 - train - INFO - Epoch: 40 | Test Loss: 0.522 | Test Acc: 82.90% +2025-03-14 16:24:10,073 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.347 | Acc: 87.50% +2025-03-14 16:24:16,616 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.431 | Acc: 85.68% +2025-03-14 16:24:23,037 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.438 | Acc: 85.39% +2025-03-14 16:24:29,562 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.435 | Acc: 85.55% +2025-03-14 16:24:37,304 - train - INFO - Epoch: 41 | Test Loss: 0.576 | Test Acc: 80.45% +2025-03-14 16:24:37,526 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.319 | Acc: 91.41% +2025-03-14 16:24:44,058 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.426 | Acc: 86.07% +2025-03-14 16:24:50,478 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.428 | Acc: 85.86% +2025-03-14 16:24:56,812 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.436 | Acc: 85.59% +2025-03-14 16:25:04,435 - train - INFO - Epoch: 42 | Test Loss: 0.617 | Test Acc: 80.75% +2025-03-14 16:25:16,446 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.413 | Acc: 83.59% +2025-03-14 16:25:23,070 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.414 | Acc: 86.35% +2025-03-14 16:25:29,554 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.418 | Acc: 86.25% +2025-03-14 16:25:35,883 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.425 | Acc: 85.97% +2025-03-14 16:25:43,587 - train - INFO - Epoch: 43 | Test Loss: 0.571 | Test Acc: 80.94% +2025-03-14 16:25:43,825 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.422 | Acc: 85.16% +2025-03-14 16:25:50,820 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.430 | Acc: 85.84% +2025-03-14 16:25:57,866 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.435 | Acc: 85.65% +2025-03-14 16:26:04,343 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.435 | Acc: 85.64% +2025-03-14 16:26:11,962 - train - INFO - Epoch: 44 | Test Loss: 0.544 | Test Acc: 81.97% +2025-03-14 16:26:23,304 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.537 | Acc: 79.69% +2025-03-14 16:26:29,717 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.416 | Acc: 86.31% +2025-03-14 16:26:36,049 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.426 | Acc: 86.04% +2025-03-14 16:26:42,335 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.433 | Acc: 85.66% +2025-03-14 16:26:50,038 - train - INFO - Epoch: 45 | Test Loss: 0.509 | Test Acc: 82.87% +2025-03-14 16:26:50,309 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.292 | Acc: 90.62% +2025-03-14 16:26:56,772 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.407 | Acc: 86.56% +2025-03-14 16:27:03,183 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.417 | Acc: 86.22% +2025-03-14 16:27:09,786 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.421 | Acc: 86.09% +2025-03-14 16:27:17,656 - train - INFO - Epoch: 46 | Test Loss: 0.557 | Test Acc: 81.43% +2025-03-14 16:27:28,839 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.359 | Acc: 89.06% +2025-03-14 16:27:35,181 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.414 | Acc: 86.39% +2025-03-14 16:27:41,630 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.427 | Acc: 85.87% +2025-03-14 16:27:47,853 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.430 | Acc: 85.79% +2025-03-14 16:27:56,187 - train - INFO - Epoch: 47 | Test Loss: 0.579 | Test Acc: 81.69% +2025-03-14 16:27:56,420 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.468 | Acc: 85.94% +2025-03-14 16:28:03,669 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.428 | Acc: 85.92% +2025-03-14 16:28:10,055 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.430 | Acc: 85.82% +2025-03-14 16:28:16,357 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.429 | Acc: 85.72% +2025-03-14 16:28:23,968 - train - INFO - Epoch: 48 | Test Loss: 0.595 | Test Acc: 80.28% +2025-03-14 16:28:35,744 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.294 | Acc: 89.06% +2025-03-14 16:28:42,339 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.401 | Acc: 86.66% +2025-03-14 16:28:48,791 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.411 | Acc: 86.20% +2025-03-14 16:28:55,258 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.418 | Acc: 86.02% +2025-03-14 16:29:02,924 - train - INFO - Epoch: 49 | Test Loss: 0.536 | Test Acc: 81.65% +2025-03-14 16:29:03,178 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.444 | Acc: 85.94% +2025-03-14 16:29:09,522 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.410 | Acc: 87.00% +2025-03-14 16:29:15,921 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.409 | Acc: 86.71% +2025-03-14 16:29:22,219 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.415 | Acc: 86.36% +2025-03-14 16:29:29,514 - train - INFO - Epoch: 50 | Test Loss: 0.619 | Test Acc: 78.84% +2025-03-14 16:29:40,209 - train - INFO - 训练完成! diff --git a/Image/DenseNet/code/train.log b/Image/DenseNet/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..6cd3b8e90eacbadd2ecc9eb3431a9087181fe66c --- /dev/null +++ b/Image/DenseNet/code/train.log @@ -0,0 +1,250 @@ +2025-03-09 21:11:49,168 - train - INFO - 开始训练 densenet +2025-03-09 21:11:49,168 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:2 +2025-03-09 21:11:49,894 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.311 | Acc: 8.59% +2025-03-09 21:11:55,683 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 1.940 | Acc: 27.50% +2025-03-09 21:12:01,584 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.770 | Acc: 34.31% +2025-03-09 21:12:07,368 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.653 | Acc: 38.88% +2025-03-09 21:12:14,425 - train - INFO - Epoch: 1 | Test Loss: 1.328 | Test Acc: 53.16% +2025-03-09 21:12:14,716 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.206 | Acc: 55.47% +2025-03-09 21:12:21,053 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.124 | Acc: 59.37% +2025-03-09 21:12:27,403 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.072 | Acc: 61.63% +2025-03-09 21:12:33,389 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.033 | Acc: 63.06% +2025-03-09 21:12:40,181 - train - INFO - Epoch: 2 | Test Loss: 1.210 | Test Acc: 61.82% +2025-03-09 21:12:49,405 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 0.909 | Acc: 71.09% +2025-03-09 21:12:56,088 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 0.830 | Acc: 70.80% +2025-03-09 21:13:03,091 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 0.805 | Acc: 71.64% +2025-03-09 21:13:09,972 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 0.787 | Acc: 72.33% +2025-03-09 21:13:17,000 - train - INFO - Epoch: 3 | Test Loss: 0.768 | Test Acc: 73.42% +2025-03-09 21:13:17,213 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 0.791 | Acc: 73.44% +2025-03-09 21:13:22,943 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 0.690 | Acc: 75.70% +2025-03-09 21:13:28,945 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 0.669 | Acc: 76.64% +2025-03-09 21:13:35,287 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 0.662 | Acc: 76.95% +2025-03-09 21:13:43,309 - train - INFO - Epoch: 4 | Test Loss: 0.685 | Test Acc: 76.06% +2025-03-09 21:13:54,007 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 0.594 | Acc: 79.69% +2025-03-09 21:14:00,201 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 0.594 | Acc: 79.16% +2025-03-09 21:14:06,592 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 0.595 | Acc: 79.42% +2025-03-09 21:14:13,084 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 0.594 | Acc: 79.52% +2025-03-09 21:14:20,411 - train - INFO - Epoch: 5 | Test Loss: 0.712 | Test Acc: 75.64% +2025-03-09 21:14:20,685 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 0.455 | Acc: 85.94% +2025-03-09 21:14:27,227 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 0.543 | Acc: 81.23% +2025-03-09 21:14:33,263 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 0.546 | Acc: 81.30% +2025-03-09 21:14:39,203 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 0.547 | Acc: 81.20% +2025-03-09 21:14:46,340 - train - INFO - Epoch: 6 | Test Loss: 0.847 | Test Acc: 73.10% +2025-03-09 21:14:56,240 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.486 | Acc: 80.47% +2025-03-09 21:15:03,070 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 0.527 | Acc: 81.69% +2025-03-09 21:15:09,779 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 0.526 | Acc: 81.86% +2025-03-09 21:15:15,807 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 0.526 | Acc: 81.90% +2025-03-09 21:15:22,494 - train - INFO - Epoch: 7 | Test Loss: 0.673 | Test Acc: 77.47% +2025-03-09 21:15:22,735 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.419 | Acc: 85.16% +2025-03-09 21:15:28,714 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.505 | Acc: 82.74% +2025-03-09 21:15:35,489 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.516 | Acc: 82.33% +2025-03-09 21:15:41,666 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.510 | Acc: 82.53% +2025-03-09 21:15:49,206 - train - INFO - Epoch: 8 | Test Loss: 0.662 | Test Acc: 77.04% +2025-03-09 21:15:59,574 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 0.538 | Acc: 80.47% +2025-03-09 21:16:06,194 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.488 | Acc: 83.35% +2025-03-09 21:16:12,155 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.490 | Acc: 83.31% +2025-03-09 21:16:18,332 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.495 | Acc: 83.14% +2025-03-09 21:16:25,285 - train - INFO - Epoch: 9 | Test Loss: 0.670 | Test Acc: 77.47% +2025-03-09 21:16:25,526 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.566 | Acc: 77.34% +2025-03-09 21:16:31,632 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.485 | Acc: 83.08% +2025-03-09 21:16:37,363 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.490 | Acc: 82.98% +2025-03-09 21:16:43,737 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.492 | Acc: 83.05% +2025-03-09 21:16:50,797 - train - INFO - Epoch: 10 | Test Loss: 0.664 | Test Acc: 77.87% +2025-03-09 21:17:00,747 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.431 | Acc: 85.94% +2025-03-09 21:17:06,577 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.477 | Acc: 83.89% +2025-03-09 21:17:12,684 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.480 | Acc: 83.71% +2025-03-09 21:17:18,559 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.480 | Acc: 83.62% +2025-03-09 21:17:25,751 - train - INFO - Epoch: 11 | Test Loss: 0.653 | Test Acc: 77.73% +2025-03-09 21:17:25,991 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.406 | Acc: 88.28% +2025-03-09 21:17:32,073 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.470 | Acc: 83.87% +2025-03-09 21:17:38,732 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.473 | Acc: 83.91% +2025-03-09 21:17:45,448 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.474 | Acc: 83.92% +2025-03-09 21:17:52,973 - train - INFO - Epoch: 12 | Test Loss: 0.609 | Test Acc: 79.12% +2025-03-09 21:18:02,605 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.588 | Acc: 82.03% +2025-03-09 21:18:09,036 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.446 | Acc: 84.57% +2025-03-09 21:18:15,491 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.460 | Acc: 84.19% +2025-03-09 21:18:21,601 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.456 | Acc: 84.31% +2025-03-09 21:18:28,561 - train - INFO - Epoch: 13 | Test Loss: 0.613 | Test Acc: 79.41% +2025-03-09 21:18:28,768 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.302 | Acc: 89.06% +2025-03-09 21:18:35,282 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.463 | Acc: 84.21% +2025-03-09 21:18:41,504 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.461 | Acc: 84.27% +2025-03-09 21:18:48,232 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.458 | Acc: 84.32% +2025-03-09 21:18:55,232 - train - INFO - Epoch: 14 | Test Loss: 0.518 | Test Acc: 81.97% +2025-03-09 21:19:04,117 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.396 | Acc: 85.94% +2025-03-09 21:19:10,215 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.436 | Acc: 84.96% +2025-03-09 21:19:16,620 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.441 | Acc: 84.67% +2025-03-09 21:19:23,308 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.446 | Acc: 84.72% +2025-03-09 21:19:31,353 - train - INFO - Epoch: 15 | Test Loss: 0.614 | Test Acc: 79.11% +2025-03-09 21:19:31,609 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.452 | Acc: 85.16% +2025-03-09 21:19:37,681 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.438 | Acc: 84.92% +2025-03-09 21:19:43,368 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.446 | Acc: 84.64% +2025-03-09 21:19:49,273 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.444 | Acc: 84.83% +2025-03-09 21:19:57,522 - train - INFO - Epoch: 16 | Test Loss: 0.552 | Test Acc: 81.27% +2025-03-09 21:20:09,354 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.323 | Acc: 90.62% +2025-03-09 21:20:15,473 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.410 | Acc: 86.25% +2025-03-09 21:20:21,552 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.439 | Acc: 85.15% +2025-03-09 21:20:27,549 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.440 | Acc: 85.08% +2025-03-09 21:20:35,037 - train - INFO - Epoch: 17 | Test Loss: 0.573 | Test Acc: 81.27% +2025-03-09 21:20:35,286 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.416 | Acc: 85.94% +2025-03-09 21:20:41,226 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.404 | Acc: 86.05% +2025-03-09 21:20:47,415 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.418 | Acc: 85.49% +2025-03-09 21:20:53,316 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.424 | Acc: 85.37% +2025-03-09 21:21:00,935 - train - INFO - Epoch: 18 | Test Loss: 0.579 | Test Acc: 80.69% +2025-03-09 21:21:11,435 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.374 | Acc: 87.50% +2025-03-09 21:21:17,597 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.432 | Acc: 85.06% +2025-03-09 21:21:23,798 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.430 | Acc: 85.17% +2025-03-09 21:21:29,790 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.433 | Acc: 84.92% +2025-03-09 21:21:38,374 - train - INFO - Epoch: 19 | Test Loss: 0.568 | Test Acc: 81.09% +2025-03-09 21:21:39,187 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.397 | Acc: 86.72% +2025-03-09 21:21:45,999 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.416 | Acc: 85.76% +2025-03-09 21:21:52,755 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.423 | Acc: 85.38% +2025-03-09 21:21:59,191 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.420 | Acc: 85.43% +2025-03-09 21:22:06,489 - train - INFO - Epoch: 20 | Test Loss: 0.549 | Test Acc: 81.92% +2025-03-09 21:22:16,199 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.380 | Acc: 89.06% +2025-03-09 21:22:22,500 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.403 | Acc: 86.45% +2025-03-09 21:22:29,352 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.408 | Acc: 86.16% +2025-03-09 21:22:35,751 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.413 | Acc: 85.82% +2025-03-09 21:22:42,795 - train - INFO - Epoch: 21 | Test Loss: 0.527 | Test Acc: 82.40% +2025-03-09 21:22:43,029 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.499 | Acc: 84.38% +2025-03-09 21:22:48,802 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.410 | Acc: 86.12% +2025-03-09 21:22:54,812 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.418 | Acc: 85.64% +2025-03-09 21:23:00,729 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.418 | Acc: 85.68% +2025-03-09 21:23:07,906 - train - INFO - Epoch: 22 | Test Loss: 0.902 | Test Acc: 73.40% +2025-03-09 21:23:17,770 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.453 | Acc: 85.16% +2025-03-09 21:23:23,789 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.410 | Acc: 86.04% +2025-03-09 21:23:29,984 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.413 | Acc: 85.85% +2025-03-09 21:23:36,210 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.407 | Acc: 86.07% +2025-03-09 21:23:43,584 - train - INFO - Epoch: 23 | Test Loss: 0.629 | Test Acc: 79.57% +2025-03-09 21:23:43,818 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.474 | Acc: 83.59% +2025-03-09 21:23:49,856 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.393 | Acc: 86.41% +2025-03-09 21:23:55,816 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.399 | Acc: 86.19% +2025-03-09 21:24:01,461 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.399 | Acc: 86.22% +2025-03-09 21:24:08,313 - train - INFO - Epoch: 24 | Test Loss: 0.539 | Test Acc: 81.81% +2025-03-09 21:24:17,795 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.485 | Acc: 83.59% +2025-03-09 21:24:23,553 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.396 | Acc: 86.18% +2025-03-09 21:24:29,445 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.402 | Acc: 86.05% +2025-03-09 21:24:35,449 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.406 | Acc: 85.98% +2025-03-09 21:24:42,460 - train - INFO - Epoch: 25 | Test Loss: 0.539 | Test Acc: 82.08% +2025-03-09 21:24:42,688 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.492 | Acc: 78.91% +2025-03-09 21:24:48,661 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.391 | Acc: 86.51% +2025-03-09 21:24:54,754 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.395 | Acc: 86.36% +2025-03-09 21:25:00,772 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.398 | Acc: 86.21% +2025-03-09 21:25:07,465 - train - INFO - Epoch: 26 | Test Loss: 0.561 | Test Acc: 80.89% +2025-03-09 21:25:17,361 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.332 | Acc: 85.94% +2025-03-09 21:25:23,617 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.387 | Acc: 86.50% +2025-03-09 21:25:29,754 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.391 | Acc: 86.45% +2025-03-09 21:25:35,426 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.394 | Acc: 86.51% +2025-03-09 21:25:42,482 - train - INFO - Epoch: 27 | Test Loss: 0.636 | Test Acc: 80.20% +2025-03-09 21:25:42,739 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.327 | Acc: 89.84% +2025-03-09 21:25:48,984 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.376 | Acc: 86.90% +2025-03-09 21:25:55,729 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.386 | Acc: 86.62% +2025-03-09 21:26:02,430 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.392 | Acc: 86.50% +2025-03-09 21:26:09,534 - train - INFO - Epoch: 28 | Test Loss: 0.604 | Test Acc: 80.52% +2025-03-09 21:26:19,870 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.393 | Acc: 89.06% +2025-03-09 21:26:26,037 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.384 | Acc: 86.73% +2025-03-09 21:26:32,204 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.388 | Acc: 86.72% +2025-03-09 21:26:38,258 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.385 | Acc: 86.78% +2025-03-09 21:26:44,989 - train - INFO - Epoch: 29 | Test Loss: 0.581 | Test Acc: 80.79% +2025-03-09 21:26:45,232 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.339 | Acc: 88.28% +2025-03-09 21:26:51,032 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.371 | Acc: 87.30% +2025-03-09 21:26:56,876 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.376 | Acc: 87.18% +2025-03-09 21:27:02,463 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.380 | Acc: 87.05% +2025-03-09 21:27:09,305 - train - INFO - Epoch: 30 | Test Loss: 0.512 | Test Acc: 82.26% +2025-03-09 21:27:19,501 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.309 | Acc: 87.50% +2025-03-09 21:27:25,645 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.367 | Acc: 87.24% +2025-03-09 21:27:32,514 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.379 | Acc: 87.03% +2025-03-09 21:27:39,392 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.386 | Acc: 86.70% +2025-03-09 21:27:46,716 - train - INFO - Epoch: 31 | Test Loss: 0.715 | Test Acc: 77.60% +2025-03-09 21:27:46,951 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.417 | Acc: 87.50% +2025-03-09 21:27:53,142 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.370 | Acc: 87.15% +2025-03-09 21:27:59,233 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.380 | Acc: 86.96% +2025-03-09 21:28:05,461 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.383 | Acc: 86.79% +2025-03-09 21:28:12,214 - train - INFO - Epoch: 32 | Test Loss: 0.581 | Test Acc: 80.21% +2025-03-09 21:28:21,517 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.527 | Acc: 82.03% +2025-03-09 21:28:27,470 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.367 | Acc: 87.20% +2025-03-09 21:28:33,329 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.382 | Acc: 86.78% +2025-03-09 21:28:39,101 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.384 | Acc: 86.76% +2025-03-09 21:28:46,139 - train - INFO - Epoch: 33 | Test Loss: 0.525 | Test Acc: 82.67% +2025-03-09 21:28:46,389 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.350 | Acc: 89.06% +2025-03-09 21:28:52,622 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.350 | Acc: 87.94% +2025-03-09 21:28:58,609 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.361 | Acc: 87.54% +2025-03-09 21:29:04,578 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.371 | Acc: 87.25% +2025-03-09 21:29:11,580 - train - INFO - Epoch: 34 | Test Loss: 0.572 | Test Acc: 81.09% +2025-03-09 21:29:21,637 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.500 | Acc: 83.59% +2025-03-09 21:29:28,349 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.363 | Acc: 87.59% +2025-03-09 21:29:34,488 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.376 | Acc: 87.18% +2025-03-09 21:29:40,398 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.380 | Acc: 87.04% +2025-03-09 21:29:48,658 - train - INFO - Epoch: 35 | Test Loss: 0.455 | Test Acc: 84.92% +2025-03-09 21:29:48,921 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.357 | Acc: 89.06% +2025-03-09 21:29:55,061 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.364 | Acc: 87.31% +2025-03-09 21:30:00,890 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.371 | Acc: 87.18% +2025-03-09 21:30:06,897 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.375 | Acc: 87.03% +2025-03-09 21:30:14,235 - train - INFO - Epoch: 36 | Test Loss: 0.541 | Test Acc: 82.74% +2025-03-09 21:30:24,234 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.431 | Acc: 84.38% +2025-03-09 21:30:29,986 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.356 | Acc: 87.55% +2025-03-09 21:30:36,139 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.370 | Acc: 87.21% +2025-03-09 21:30:42,107 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.371 | Acc: 87.20% +2025-03-09 21:30:49,263 - train - INFO - Epoch: 37 | Test Loss: 0.514 | Test Acc: 82.69% +2025-03-09 21:30:49,531 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.229 | Acc: 90.62% +2025-03-09 21:30:55,563 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.348 | Acc: 87.84% +2025-03-09 21:31:01,689 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.358 | Acc: 87.62% +2025-03-09 21:31:08,168 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.368 | Acc: 87.41% +2025-03-09 21:31:15,751 - train - INFO - Epoch: 38 | Test Loss: 0.497 | Test Acc: 83.43% +2025-03-09 21:31:25,606 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.285 | Acc: 92.97% +2025-03-09 21:31:31,509 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.361 | Acc: 87.68% +2025-03-09 21:31:37,804 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.362 | Acc: 87.66% +2025-03-09 21:31:44,177 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.362 | Acc: 87.68% +2025-03-09 21:31:50,975 - train - INFO - Epoch: 39 | Test Loss: 0.527 | Test Acc: 82.27% +2025-03-09 21:31:51,210 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.305 | Acc: 89.06% +2025-03-09 21:31:56,699 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.352 | Acc: 87.79% +2025-03-09 21:32:02,349 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.361 | Acc: 87.46% +2025-03-09 21:32:08,110 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.369 | Acc: 87.15% +2025-03-09 21:32:15,326 - train - INFO - Epoch: 40 | Test Loss: 0.653 | Test Acc: 78.55% +2025-03-09 21:32:25,211 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.522 | Acc: 82.03% +2025-03-09 21:32:30,421 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.367 | Acc: 87.38% +2025-03-09 21:32:36,104 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.363 | Acc: 87.40% +2025-03-09 21:32:42,045 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.363 | Acc: 87.31% +2025-03-09 21:32:49,160 - train - INFO - Epoch: 41 | Test Loss: 0.510 | Test Acc: 82.85% +2025-03-09 21:32:49,411 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.325 | Acc: 87.50% +2025-03-09 21:32:55,294 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.354 | Acc: 87.75% +2025-03-09 21:33:00,458 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.364 | Acc: 87.59% +2025-03-09 21:33:06,072 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.363 | Acc: 87.61% +2025-03-09 21:33:12,986 - train - INFO - Epoch: 42 | Test Loss: 0.555 | Test Acc: 81.95% +2025-03-09 21:33:23,293 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.384 | Acc: 84.38% +2025-03-09 21:33:28,557 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.356 | Acc: 87.80% +2025-03-09 21:33:34,297 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.364 | Acc: 87.48% +2025-03-09 21:33:40,033 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.363 | Acc: 87.58% +2025-03-09 21:33:47,053 - train - INFO - Epoch: 43 | Test Loss: 0.467 | Test Acc: 84.13% +2025-03-09 21:33:47,325 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.285 | Acc: 89.84% +2025-03-09 21:33:53,575 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.343 | Acc: 88.06% +2025-03-09 21:33:59,731 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.350 | Acc: 87.93% +2025-03-09 21:34:05,556 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.355 | Acc: 87.73% +2025-03-09 21:34:12,656 - train - INFO - Epoch: 44 | Test Loss: 0.767 | Test Acc: 74.93% +2025-03-09 21:34:23,508 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.393 | Acc: 83.59% +2025-03-09 21:34:29,421 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.347 | Acc: 87.89% +2025-03-09 21:34:35,133 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.342 | Acc: 88.16% +2025-03-09 21:34:40,818 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.350 | Acc: 87.92% +2025-03-09 21:34:47,264 - train - INFO - Epoch: 45 | Test Loss: 0.480 | Test Acc: 83.56% +2025-03-09 21:34:47,512 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.302 | Acc: 89.06% +2025-03-09 21:34:53,220 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.342 | Acc: 88.10% +2025-03-09 21:34:59,094 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.347 | Acc: 88.02% +2025-03-09 21:35:05,396 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.348 | Acc: 88.04% +2025-03-09 21:35:11,923 - train - INFO - Epoch: 46 | Test Loss: 0.448 | Test Acc: 84.89% +2025-03-09 21:35:22,070 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.401 | Acc: 86.72% +2025-03-09 21:35:28,150 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.347 | Acc: 87.89% +2025-03-09 21:35:34,314 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.354 | Acc: 87.75% +2025-03-09 21:35:40,069 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.357 | Acc: 87.63% +2025-03-09 21:35:46,827 - train - INFO - Epoch: 47 | Test Loss: 0.508 | Test Acc: 82.58% +2025-03-09 21:35:47,066 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.263 | Acc: 91.41% +2025-03-09 21:35:53,455 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.330 | Acc: 88.49% +2025-03-09 21:35:59,739 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.336 | Acc: 88.37% +2025-03-09 21:36:05,768 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.340 | Acc: 88.33% +2025-03-09 21:36:12,946 - train - INFO - Epoch: 48 | Test Loss: 0.432 | Test Acc: 84.88% +2025-03-09 21:36:22,692 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.347 | Acc: 89.06% +2025-03-09 21:36:28,875 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.335 | Acc: 88.68% +2025-03-09 21:36:34,927 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.336 | Acc: 88.46% +2025-03-09 21:36:40,251 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.342 | Acc: 88.42% +2025-03-09 21:36:47,402 - train - INFO - Epoch: 49 | Test Loss: 0.419 | Test Acc: 85.76% +2025-03-09 21:36:47,660 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.199 | Acc: 93.75% +2025-03-09 21:36:53,975 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.328 | Acc: 88.47% +2025-03-09 21:36:59,800 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.342 | Acc: 88.16% diff --git a/Image/DenseNet/code/train.py b/Image/DenseNet/code/train.py index ffaa8fe82f30f67c9bd8e3e66f16f8cfa1aec4c2..88d158e41f9d0b599535b3c746811ab81a78006f 100644 --- a/Image/DenseNet/code/train.py +++ b/Image/DenseNet/code/train.py @@ -21,12 +21,14 @@ def main(): model=model, trainloader=trainloader, testloader=testloader, - epochs=args.epochs, - lr=args.lr, + epochs=50, + lr=0.1, device=f'cuda:{args.gpu}', save_dir='../model', model_name='densenet', - save_type='0' + save_type='0', + layer_name='avg_pool', + interval = 2 ) elif args.train_type == '1': train_model_data_augmentation( @@ -45,14 +47,16 @@ def main(): model, poison_ratio=args.poison_ratio, target_label=args.target_label, - epochs=args.epochs, - lr=args.lr, + epochs=50, + lr=0.1, device=f'cuda:{args.gpu}', save_dir='../model', model_name='densenet', batch_size=args.batch_size, num_workers=args.num_workers, - local_dataset_path=args.dataset_path + local_dataset_path=args.dataset_path, + layer_name='avg_pool' , + interval = 2 ) if __name__ == '__main__': diff --git a/Image/DenseNet/model/0/epoch1/embeddings.npy b/Image/DenseNet/model/0/epoch1/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..32da30892ebdfdef2da1b546d11f2ba44d4f99f7 --- /dev/null +++ b/Image/DenseNet/model/0/epoch1/embeddings.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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Loss: 2.138 | Acc: 18.65% +2025-03-14 16:11:36,178 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 2.103 | Acc: 20.15% +2025-03-14 16:11:44,414 - train - INFO - Epoch: 2 | Test Loss: 1.911 | Test Acc: 28.45% diff --git a/Image/EfficientNet/code/train.log b/Image/EfficientNet/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..6358853bed4659dfc526b4cddb83247f30ffcc41 --- /dev/null +++ b/Image/EfficientNet/code/train.log @@ -0,0 +1,253 @@ +2025-03-14 16:24:53,316 - train - INFO - 开始训练 efficientnet +2025-03-14 16:24:53,316 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:2 +2025-03-14 16:24:54,150 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.403 | Acc: 8.59% +2025-03-14 16:24:58,097 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.611 | Acc: 16.51% +2025-03-14 16:25:02,317 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.378 | Acc: 18.66% +2025-03-14 16:25:06,595 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 2.223 | Acc: 21.99% +2025-03-14 16:25:11,907 - train - INFO - Epoch: 1 | Test Loss: 1.709 | Test Acc: 37.62% +2025-03-14 16:25:12,408 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.729 | Acc: 31.25% +2025-03-14 16:25:16,797 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.697 | Acc: 36.48% +2025-03-14 16:25:21,076 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.662 | Acc: 38.39% +2025-03-14 16:25:25,190 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.639 | Acc: 39.51% +2025-03-14 16:25:30,403 - train - INFO - Epoch: 2 | Test Loss: 1.489 | Test Acc: 46.71% +2025-03-14 16:25:40,451 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.456 | Acc: 48.44% +2025-03-14 16:25:44,586 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.506 | Acc: 45.27% +2025-03-14 16:25:49,037 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.490 | Acc: 46.16% +2025-03-14 16:25:53,242 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.464 | Acc: 47.02% +2025-03-14 16:25:58,157 - train - INFO - Epoch: 3 | Test Loss: 1.362 | Test Acc: 50.80% +2025-03-14 16:25:58,372 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.323 | Acc: 48.44% +2025-03-14 16:26:02,660 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.341 | Acc: 51.55% +2025-03-14 16:26:06,851 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.326 | Acc: 52.31% +2025-03-14 16:26:11,085 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.311 | Acc: 52.83% +2025-03-14 16:26:16,103 - train - INFO - Epoch: 4 | Test Loss: 1.226 | Test Acc: 54.20% +2025-03-14 16:26:25,973 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.249 | Acc: 53.12% +2025-03-14 16:26:30,091 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.228 | Acc: 55.75% +2025-03-14 16:26:34,122 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.233 | Acc: 55.84% +2025-03-14 16:26:38,125 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.222 | Acc: 56.30% +2025-03-14 16:26:43,375 - train - INFO - Epoch: 5 | Test Loss: 1.064 | Test Acc: 62.23% +2025-03-14 16:26:43,613 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.153 | Acc: 67.97% +2025-03-14 16:26:47,694 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.118 | Acc: 60.44% +2025-03-14 16:26:52,049 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.114 | Acc: 60.63% +2025-03-14 16:26:56,398 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.101 | Acc: 60.95% +2025-03-14 16:27:01,593 - train - INFO - Epoch: 6 | Test Loss: 1.013 | Test Acc: 64.31% +2025-03-14 16:27:11,823 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 0.962 | Acc: 65.62% +2025-03-14 16:27:15,984 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.026 | Acc: 63.78% +2025-03-14 16:27:20,007 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.023 | Acc: 64.04% +2025-03-14 16:27:24,044 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.011 | Acc: 64.39% +2025-03-14 16:27:29,341 - train - INFO - Epoch: 7 | Test Loss: 0.982 | Test Acc: 66.05% +2025-03-14 16:27:29,554 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 0.997 | Acc: 62.50% +2025-03-14 16:27:33,742 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 0.977 | Acc: 65.49% +2025-03-14 16:27:37,968 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 0.972 | Acc: 65.79% +2025-03-14 16:27:41,908 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 0.967 | Acc: 66.00% +2025-03-14 16:27:47,093 - train - INFO - Epoch: 8 | Test Loss: 0.970 | Test Acc: 65.27% +2025-03-14 16:27:58,459 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.097 | Acc: 61.72% +2025-03-14 16:28:03,091 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 0.933 | Acc: 67.14% +2025-03-14 16:28:07,115 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 0.928 | Acc: 67.26% +2025-03-14 16:28:11,125 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 0.922 | Acc: 67.64% +2025-03-14 16:28:16,262 - train - INFO - Epoch: 9 | Test Loss: 0.860 | Test Acc: 70.44% +2025-03-14 16:28:16,484 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 0.762 | Acc: 69.53% +2025-03-14 16:28:20,620 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 0.881 | Acc: 69.64% +2025-03-14 16:28:24,782 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 0.881 | Acc: 69.51% +2025-03-14 16:28:29,178 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 0.883 | Acc: 69.31% +2025-03-14 16:28:34,443 - train - INFO - Epoch: 10 | Test Loss: 0.961 | Test Acc: 67.11% +2025-03-14 16:28:44,377 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 0.838 | Acc: 65.62% +2025-03-14 16:28:48,450 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 0.856 | Acc: 70.35% +2025-03-14 16:28:52,422 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 0.853 | Acc: 70.38% +2025-03-14 16:28:56,648 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 0.859 | Acc: 70.10% +2025-03-14 16:29:01,922 - train - INFO - Epoch: 11 | Test Loss: 0.929 | Test Acc: 68.30% +2025-03-14 16:29:02,164 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 0.918 | Acc: 68.75% +2025-03-14 16:29:06,383 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 0.843 | Acc: 70.23% +2025-03-14 16:29:10,393 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 0.849 | Acc: 70.18% +2025-03-14 16:29:14,619 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 0.851 | Acc: 70.25% +2025-03-14 16:29:20,040 - train - INFO - Epoch: 12 | Test Loss: 0.890 | Test Acc: 69.71% +2025-03-14 16:29:30,009 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 0.686 | Acc: 77.34% +2025-03-14 16:29:34,151 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 0.832 | Acc: 71.06% +2025-03-14 16:29:38,169 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 0.826 | Acc: 71.05% +2025-03-14 16:29:42,352 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 0.831 | Acc: 70.93% +2025-03-14 16:29:47,612 - train - INFO - Epoch: 13 | Test Loss: 0.804 | Test Acc: 72.10% +2025-03-14 16:29:47,842 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 0.855 | Acc: 68.75% +2025-03-14 16:29:52,017 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 0.805 | Acc: 71.96% +2025-03-14 16:29:55,912 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 0.814 | Acc: 71.71% +2025-03-14 16:30:00,027 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 0.818 | Acc: 71.49% +2025-03-14 16:30:05,322 - train - INFO - Epoch: 14 | Test Loss: 0.828 | Test Acc: 70.77% +2025-03-14 16:30:15,164 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.886 | Acc: 71.09% +2025-03-14 16:30:19,341 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 0.822 | Acc: 72.01% +2025-03-14 16:30:23,394 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 0.812 | Acc: 72.11% +2025-03-14 16:30:27,423 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 0.806 | Acc: 72.18% +2025-03-14 16:30:32,746 - train - INFO - Epoch: 15 | Test Loss: 0.792 | Test Acc: 72.85% +2025-03-14 16:30:32,981 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.680 | Acc: 77.34% +2025-03-14 16:30:37,197 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 0.772 | Acc: 73.04% +2025-03-14 16:30:41,453 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 0.785 | Acc: 72.70% +2025-03-14 16:30:45,667 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 0.786 | Acc: 72.58% +2025-03-14 16:30:50,860 - train - INFO - Epoch: 16 | Test Loss: 0.849 | Test Acc: 69.94% +2025-03-14 16:31:00,847 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.778 | Acc: 67.97% +2025-03-14 16:31:05,109 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 0.779 | Acc: 72.93% +2025-03-14 16:31:09,247 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 0.775 | Acc: 73.05% +2025-03-14 16:31:13,118 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 0.775 | Acc: 73.09% +2025-03-14 16:31:18,274 - train - INFO - Epoch: 17 | Test Loss: 0.962 | Test Acc: 68.02% +2025-03-14 16:31:18,490 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 0.703 | Acc: 71.09% +2025-03-14 16:31:22,475 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 0.768 | Acc: 73.15% +2025-03-14 16:31:26,283 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 0.768 | Acc: 73.13% +2025-03-14 16:31:30,181 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 0.773 | Acc: 73.08% +2025-03-14 16:31:35,186 - train - INFO - Epoch: 18 | Test Loss: 0.800 | Test Acc: 72.60% +2025-03-14 16:31:44,925 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 0.683 | Acc: 74.22% +2025-03-14 16:31:48,912 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 0.755 | Acc: 74.02% +2025-03-14 16:31:52,928 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 0.757 | Acc: 73.93% +2025-03-14 16:31:56,816 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 0.759 | Acc: 73.83% +2025-03-14 16:32:01,848 - train - INFO - Epoch: 19 | Test Loss: 0.843 | Test Acc: 70.60% +2025-03-14 16:32:02,083 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 0.831 | Acc: 69.53% +2025-03-14 16:32:06,173 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 0.762 | Acc: 73.55% +2025-03-14 16:32:10,108 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 0.761 | Acc: 73.54% +2025-03-14 16:32:13,922 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 0.754 | Acc: 73.83% +2025-03-14 16:32:18,890 - train - INFO - Epoch: 20 | Test Loss: 0.725 | Test Acc: 75.27% +2025-03-14 16:32:28,431 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 0.911 | Acc: 68.75% +2025-03-14 16:32:32,648 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 0.767 | Acc: 73.45% +2025-03-14 16:32:36,693 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 0.747 | Acc: 74.06% +2025-03-14 16:32:40,812 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 0.750 | Acc: 74.00% +2025-03-14 16:32:45,986 - train - INFO - Epoch: 21 | Test Loss: 0.778 | Test Acc: 73.44% +2025-03-14 16:32:46,219 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.708 | Acc: 76.56% +2025-03-14 16:32:50,396 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 0.760 | Acc: 73.92% +2025-03-14 16:32:54,576 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 0.745 | Acc: 74.22% +2025-03-14 16:32:58,677 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 0.742 | Acc: 74.32% +2025-03-14 16:33:03,987 - train - INFO - Epoch: 22 | Test Loss: 0.729 | Test Acc: 74.40% +2025-03-14 16:33:13,962 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 0.711 | Acc: 75.78% +2025-03-14 16:33:18,261 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 0.737 | Acc: 74.22% +2025-03-14 16:33:22,344 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 0.729 | Acc: 74.59% +2025-03-14 16:33:26,554 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 0.736 | Acc: 74.30% +2025-03-14 16:33:31,759 - train - INFO - Epoch: 23 | Test Loss: 0.795 | Test Acc: 72.91% +2025-03-14 16:33:31,969 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 0.667 | Acc: 78.12% +2025-03-14 16:33:36,099 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 0.708 | Acc: 75.29% +2025-03-14 16:33:40,299 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 0.716 | Acc: 74.91% +2025-03-14 16:33:44,520 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 0.720 | Acc: 74.79% +2025-03-14 16:33:49,851 - train - INFO - Epoch: 24 | Test Loss: 0.713 | Test Acc: 75.64% +2025-03-14 16:33:59,792 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 0.801 | Acc: 75.00% +2025-03-14 16:34:03,785 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 0.717 | Acc: 75.38% +2025-03-14 16:34:07,807 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 0.718 | Acc: 75.12% +2025-03-14 16:34:11,748 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 0.717 | Acc: 75.06% +2025-03-14 16:34:16,783 - train - INFO - Epoch: 25 | Test Loss: 0.719 | Test Acc: 75.11% +2025-03-14 16:34:16,994 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.749 | Acc: 72.66% +2025-03-14 16:34:21,113 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 0.717 | Acc: 75.18% +2025-03-14 16:34:25,235 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 0.716 | Acc: 75.06% +2025-03-14 16:34:29,185 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 0.714 | Acc: 75.23% +2025-03-14 16:34:34,159 - train - INFO - Epoch: 26 | Test Loss: 0.717 | Test Acc: 75.26% +2025-03-14 16:34:44,151 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 0.634 | Acc: 79.69% +2025-03-14 16:34:48,111 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 0.690 | Acc: 76.35% +2025-03-14 16:34:52,137 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 0.702 | Acc: 75.84% +2025-03-14 16:34:56,098 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 0.707 | Acc: 75.66% +2025-03-14 16:35:01,194 - train - INFO - Epoch: 27 | Test Loss: 0.782 | Test Acc: 74.84% +2025-03-14 16:35:01,448 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 0.664 | Acc: 79.69% +2025-03-14 16:35:05,614 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 0.718 | Acc: 75.27% +2025-03-14 16:35:09,598 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 0.708 | Acc: 75.55% +2025-03-14 16:35:13,509 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 0.706 | Acc: 75.58% +2025-03-14 16:35:18,618 - train - INFO - Epoch: 28 | Test Loss: 0.711 | Test Acc: 75.23% +2025-03-14 16:35:29,152 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 0.696 | Acc: 75.78% +2025-03-14 16:35:33,789 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 0.688 | Acc: 76.26% +2025-03-14 16:35:38,060 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 0.690 | Acc: 76.25% +2025-03-14 16:35:42,470 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 0.695 | Acc: 75.97% +2025-03-14 16:35:48,049 - train - INFO - Epoch: 29 | Test Loss: 0.731 | Test Acc: 74.49% +2025-03-14 16:35:48,286 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.733 | Acc: 74.22% +2025-03-14 16:35:52,658 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 0.690 | Acc: 76.14% +2025-03-14 16:35:57,013 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 0.693 | Acc: 76.10% +2025-03-14 16:36:01,138 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 0.690 | Acc: 76.22% +2025-03-14 16:36:06,430 - train - INFO - Epoch: 30 | Test Loss: 0.801 | Test Acc: 72.13% +2025-03-14 16:36:17,359 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.648 | Acc: 79.69% +2025-03-14 16:36:22,033 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 0.680 | Acc: 76.55% +2025-03-14 16:36:26,594 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 0.691 | Acc: 76.15% +2025-03-14 16:36:31,032 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 0.681 | Acc: 76.51% +2025-03-14 16:36:36,579 - train - INFO - Epoch: 31 | Test Loss: 0.677 | Test Acc: 76.25% +2025-03-14 16:36:36,805 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.527 | Acc: 83.59% +2025-03-14 16:36:41,318 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 0.686 | Acc: 75.84% +2025-03-14 16:36:46,490 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 0.687 | Acc: 75.98% +2025-03-14 16:36:51,387 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 0.686 | Acc: 76.03% +2025-03-14 16:36:57,281 - train - INFO - Epoch: 32 | Test Loss: 0.690 | Test Acc: 76.28% +2025-03-14 16:37:08,203 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 0.722 | Acc: 76.56% +2025-03-14 16:37:13,143 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 0.680 | Acc: 76.66% +2025-03-14 16:37:17,799 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 0.678 | Acc: 76.55% +2025-03-14 16:37:22,467 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 0.685 | Acc: 76.24% +2025-03-14 16:37:28,280 - train - INFO - Epoch: 33 | Test Loss: 0.681 | Test Acc: 76.51% +2025-03-14 16:37:28,543 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 0.765 | Acc: 72.66% +2025-03-14 16:37:33,136 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 0.647 | Acc: 77.37% +2025-03-14 16:37:37,717 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 0.670 | Acc: 76.81% +2025-03-14 16:37:42,617 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 0.674 | Acc: 76.67% +2025-03-14 16:37:48,408 - train - INFO - Epoch: 34 | Test Loss: 0.821 | Test Acc: 72.42% +2025-03-14 16:37:58,925 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 0.620 | Acc: 76.56% +2025-03-14 16:38:03,539 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 0.674 | Acc: 76.53% +2025-03-14 16:38:08,136 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 0.671 | Acc: 76.74% +2025-03-14 16:38:12,743 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 0.667 | Acc: 76.92% +2025-03-14 16:38:18,488 - train - INFO - Epoch: 35 | Test Loss: 0.712 | Test Acc: 75.76% +2025-03-14 16:38:18,764 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.744 | Acc: 72.66% +2025-03-14 16:38:23,452 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 0.670 | Acc: 77.07% +2025-03-14 16:38:28,175 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 0.658 | Acc: 77.18% +2025-03-14 16:38:33,144 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 0.665 | Acc: 77.00% +2025-03-14 16:38:38,679 - train - INFO - Epoch: 36 | Test Loss: 0.722 | Test Acc: 75.42% +2025-03-14 16:38:48,563 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 0.754 | Acc: 72.66% +2025-03-14 16:38:52,994 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 0.643 | Acc: 77.85% +2025-03-14 16:38:57,465 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 0.638 | Acc: 78.08% +2025-03-14 16:39:01,803 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 0.647 | Acc: 77.80% +2025-03-14 16:39:07,172 - train - INFO - Epoch: 37 | Test Loss: 0.681 | Test Acc: 76.38% +2025-03-14 16:39:07,411 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.838 | Acc: 69.53% +2025-03-14 16:39:11,854 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 0.642 | Acc: 77.89% +2025-03-14 16:39:16,355 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 0.652 | Acc: 77.73% +2025-03-14 16:39:20,774 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 0.659 | Acc: 77.39% +2025-03-14 16:39:26,358 - train - INFO - Epoch: 38 | Test Loss: 0.669 | Test Acc: 77.11% +2025-03-14 16:39:38,516 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 0.539 | Acc: 80.47% +2025-03-14 16:39:42,978 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 0.642 | Acc: 77.62% +2025-03-14 16:39:47,405 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 0.651 | Acc: 77.45% +2025-03-14 16:39:51,945 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 0.651 | Acc: 77.46% +2025-03-14 16:39:57,451 - train - INFO - Epoch: 39 | Test Loss: 0.739 | Test Acc: 75.44% +2025-03-14 16:39:57,707 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 0.652 | Acc: 76.56% +2025-03-14 16:40:02,251 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 0.637 | Acc: 77.98% +2025-03-14 16:40:06,685 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 0.643 | Acc: 77.82% +2025-03-14 16:40:11,120 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 0.649 | Acc: 77.58% +2025-03-14 16:40:16,706 - train - INFO - Epoch: 40 | Test Loss: 0.665 | Test Acc: 77.24% +2025-03-14 16:40:26,700 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.640 | Acc: 75.00% +2025-03-14 16:40:31,256 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 0.632 | Acc: 78.02% +2025-03-14 16:40:36,002 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 0.639 | Acc: 77.91% +2025-03-14 16:40:40,669 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 0.644 | Acc: 77.66% +2025-03-14 16:40:46,866 - train - INFO - Epoch: 41 | Test Loss: 0.657 | Test Acc: 77.40% +2025-03-14 16:40:47,109 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.586 | Acc: 80.47% +2025-03-14 16:40:52,054 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 0.630 | Acc: 78.36% +2025-03-14 16:40:56,728 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 0.642 | Acc: 77.88% +2025-03-14 16:41:01,135 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 0.647 | Acc: 77.72% +2025-03-14 16:41:06,646 - train - INFO - Epoch: 42 | Test Loss: 0.626 | Test Acc: 78.26% +2025-03-14 16:41:16,468 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.777 | Acc: 71.88% +2025-03-14 16:41:21,005 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 0.630 | Acc: 78.60% +2025-03-14 16:41:25,543 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 0.638 | Acc: 78.19% +2025-03-14 16:41:29,970 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 0.637 | Acc: 78.23% +2025-03-14 16:41:35,513 - train - INFO - Epoch: 43 | Test Loss: 0.659 | Test Acc: 77.86% +2025-03-14 16:41:35,761 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 0.658 | Acc: 74.22% +2025-03-14 16:41:40,313 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 0.645 | Acc: 77.85% +2025-03-14 16:41:44,720 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 0.634 | Acc: 78.37% +2025-03-14 16:41:49,081 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 0.639 | Acc: 78.13% +2025-03-14 16:41:54,698 - train - INFO - Epoch: 44 | Test Loss: 0.792 | Test Acc: 72.59% +2025-03-14 16:42:05,706 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 0.531 | Acc: 84.38% +2025-03-14 16:42:10,551 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 0.620 | Acc: 78.21% +2025-03-14 16:42:15,461 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 0.630 | Acc: 78.25% +2025-03-14 16:42:19,911 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 0.628 | Acc: 78.37% +2025-03-14 16:42:25,495 - train - INFO - Epoch: 45 | Test Loss: 0.655 | Test Acc: 77.37% +2025-03-14 16:42:25,737 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 0.602 | Acc: 78.12% +2025-03-14 16:42:30,584 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 0.619 | Acc: 78.44% +2025-03-14 16:42:35,258 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 0.622 | Acc: 78.49% +2025-03-14 16:42:39,743 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 0.623 | Acc: 78.62% +2025-03-14 16:42:45,591 - train - INFO - Epoch: 46 | Test Loss: 0.773 | Test Acc: 74.10% +2025-03-14 16:42:55,831 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 0.802 | Acc: 71.09% +2025-03-14 16:43:00,591 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 0.619 | Acc: 78.91% +2025-03-14 16:43:05,170 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 0.629 | Acc: 78.33% +2025-03-14 16:43:09,942 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 0.622 | Acc: 78.56% +2025-03-14 16:43:15,560 - train - INFO - Epoch: 47 | Test Loss: 0.729 | Test Acc: 75.41% +2025-03-14 16:43:15,823 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 0.684 | Acc: 75.78% +2025-03-14 16:43:20,316 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 0.621 | Acc: 78.23% +2025-03-14 16:43:24,876 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 0.619 | Acc: 78.47% +2025-03-14 16:43:29,403 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 0.619 | Acc: 78.62% +2025-03-14 16:43:35,050 - train - INFO - Epoch: 48 | Test Loss: 0.661 | Test Acc: 77.18% +2025-03-14 16:43:45,649 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.794 | Acc: 72.66% +2025-03-14 16:43:50,300 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 0.602 | Acc: 79.48% +2025-03-14 16:43:54,809 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 0.614 | Acc: 78.90% +2025-03-14 16:43:59,457 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 0.611 | Acc: 78.98% +2025-03-14 16:44:05,989 - train - INFO - Epoch: 49 | Test Loss: 0.698 | Test Acc: 76.72% +2025-03-14 16:44:06,255 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.631 | Acc: 78.12% +2025-03-14 16:44:11,479 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 0.620 | Acc: 78.37% +2025-03-14 16:44:16,041 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 0.625 | Acc: 78.28% +2025-03-14 16:44:20,488 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 0.618 | Acc: 78.60% +2025-03-14 16:44:26,092 - train - INFO - Epoch: 50 | Test Loss: 0.699 | Test Acc: 76.03% +2025-03-14 16:44:37,169 - train - INFO - 训练完成! diff --git a/Image/EfficientNet/code/train.py b/Image/EfficientNet/code/train.py index 12a767193e135185e2ad6992906791b3d9cb71f7..41115f6c0deddc49fc7408cf08de0db1b687da29 100644 --- a/Image/EfficientNet/code/train.py +++ b/Image/EfficientNet/code/train.py @@ -26,7 +26,9 @@ def main(): device=f'cuda:{args.gpu}', save_dir='../model', model_name='efficientnet', - save_type='0' + save_type='0', + layer_name='layers.15.conv3', + interval = 2 ) elif args.train_type == '1': train_model_data_augmentation( @@ -52,7 +54,9 @@ def main(): model_name='efficientnet', batch_size=args.batch_size, num_workers=args.num_workers, - local_dataset_path=args.dataset_path + local_dataset_path=args.dataset_path, + layer_name='layers.15.conv3', + interval = 2 ) if __name__ == '__main__': diff --git a/Image/EfficientNet/model/0/epoch1/embeddings.npy b/Image/EfficientNet/model/0/epoch1/embeddings.npy new file mode 100644 index 0000000000000000000000000000000000000000..715e1cd33085313329a005df972eb0ff8bc0cdf8 --- /dev/null +++ b/Image/EfficientNet/model/0/epoch1/embeddings.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc0b1c5c08e4db0742b53ef42849291d0819af52d7a4d2db9d7974cb3fbb063d +size 256000128 diff --git a/Image/EfficientNet/model/0/epoch1/subject_model.pth b/Image/EfficientNet/model/0/epoch1/subject_model.pth new file mode 100644 index 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a/Image/EfficientNet/model/0/epoch9/subject_model.pth b/Image/EfficientNet/model/0/epoch9/subject_model.pth new file mode 100644 index 0000000000000000000000000000000000000000..5a17ce42b771055c710b92ab517d8dcbb377d35f --- /dev/null +++ b/Image/EfficientNet/model/0/epoch9/subject_model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:07947ee5a8ca8742b35d164298464fcb9058605bf1c37aa5cf766c98640d4b23 +size 14676202 diff --git a/Image/EfficientNet/model/0/layer_info.json b/Image/EfficientNet/model/0/layer_info.json new file mode 100644 index 0000000000000000000000000000000000000000..9a5e7330eaf38a8e94e204d10b7ecad4bfb09a7e --- /dev/null +++ b/Image/EfficientNet/model/0/layer_info.json @@ -0,0 +1 @@ +{"layer_id": "layers.15.conv3", "dim": 1280} \ No newline at end of file diff --git a/Image/GoogLeNet/code/train.log b/Image/GoogLeNet/code/train.log new file mode 100644 index 0000000000000000000000000000000000000000..087d2c255de5719facf3ef0de3883612a0109bcf --- /dev/null +++ b/Image/GoogLeNet/code/train.log @@ -0,0 +1,12 @@ +2025-03-14 16:40:38,225 - train - INFO - 开始训练 googlenet +2025-03-14 16:40:38,226 - train - INFO - 总轮数: 100, 学习率: 0.1, 设备: cuda:3 +2025-03-14 16:40:39,249 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.328 | Acc: 11.72% +2025-03-14 16:40:46,776 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 1.935 | Acc: 29.27% +2025-03-14 16:40:53,994 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.738 | Acc: 35.56% +2025-03-14 16:41:00,949 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.609 | Acc: 40.49% +2025-03-14 16:41:09,249 - train - INFO - Epoch: 1 | Test Loss: 1.217 | Test Acc: 55.88% +2025-03-14 16:41:09,727 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 0.988 | Acc: 66.41% +2025-03-14 16:41:16,664 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.111 | Acc: 60.37% +2025-03-14 16:41:23,604 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.076 | Acc: 61.55% +2025-03-14 16:41:30,565 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.033 | Acc: 63.19% +2025-03-14 16:41:38,717 - train - INFO - Epoch: 2 | Test Loss: 1.281 | Test Acc: 59.38% diff --git a/Image/GoogLeNet/code/train.py b/Image/GoogLeNet/code/train.py index bb4ff6a0a9d535c274e62b517d6e933d43360993..37f277f4f37d01f89ccc1b9dbe1268ec11a63325 100644 --- a/Image/GoogLeNet/code/train.py +++ b/Image/GoogLeNet/code/train.py @@ -26,7 +26,9 @@ def main(): device=f'cuda:{args.gpu}', save_dir='../model', model_name='googlenet', - save_type='0' + save_type='0', + layer_name='avgpool', + interval = 2 ) elif args.train_type == '1': train_model_data_augmentation( @@ -52,7 +54,9 @@ def main(): model_name='googlenet', batch_size=args.batch_size, num_workers=args.num_workers, - local_dataset_path=args.dataset_path + local_dataset_path=args.dataset_path, + layer_name='avgpool', + interval = 2 ) if __name__ == '__main__': diff --git a/Image/utils/train_utils.py b/Image/utils/train_utils.py index e94d9f3ab4f4db9f103d6901214e474afa859c95..23ee83897d9d11ee02de5575c9d3655be46847fa 100644 --- a/Image/utils/train_utils.py +++ b/Image/utils/train_utils.py @@ -65,7 +65,7 @@ def setup_logger(log_file): return logger def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda:0', - save_dir='./checkpoints', model_name='model', save_type='0',layer_name=None): + save_dir='./checkpoints', model_name='model', save_type='0',layer_name=None,interval = 2): """通用的模型训练函数 Args: model: 要训练的模型 @@ -191,8 +191,18 @@ def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda logger.info(f'Epoch: {epoch+1} | Test Loss: {test_loss/(batch_idx+1):.3f} | ' f'Test Acc: {acc:.2f}%') + + if epoch == 0: + ordered_loader = torch.utils.data.DataLoader( + trainloader.dataset, # 使用相同的数据集 + batch_size=trainloader.batch_size, + shuffle=False, # 确保顺序加载 + num_workers=trainloader.num_workers + ) + save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name, show= True ) + # 每5个epoch保存一次 - if (epoch + 1) % 4 == 0: + if (epoch + 1) % interval == 0: # 创建一个专门用于收集embedding的顺序dataloader ordered_loader = torch.utils.data.DataLoader( trainloader.dataset, # 使用相同的数据集 @@ -200,7 +210,7 @@ def train_model(model, trainloader, testloader, epochs=200, lr=0.1, device='cuda shuffle=False, # 确保顺序加载 num_workers=trainloader.num_workers ) - save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name) + save_model = time_travel_saver(model, ordered_loader, device, save_dir, model_name, interval = 1, auto_save_embedding = True, layer_name = layer_name ) save_model.save() scheduler.step() @@ -265,7 +275,7 @@ def train_model_data_augmentation(model, epochs=200, lr=0.1, device='cuda:0', def train_model_backdoor(model, poison_ratio=0.1, target_label=0, epochs=200, lr=0.1, device='cuda:0', save_dir='./checkpoints', model_name='model', - batch_size=128, num_workers=2, local_dataset_path=None, layer_name=None): + batch_size=128, num_workers=2, local_dataset_path=None, layer_name=None,interval = 2): """训练带后门的模型 后门攻击方案说明： @@ -317,7 +327,7 @@ def train_model_backdoor(model, poison_ratio=0.1, target_label=0, epochs=200, lr trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers) # 训练模型 - train_model(model, poisoned_trainloader, testloader, epochs, lr, device, save_dir, model_name, save_type='2', layer_name=layer_name) + train_model(model, poisoned_trainloader, testloader, epochs, lr, device, save_dir, model_name, save_type='2', layer_name=layer_name,interval = interval) # 恢复原始数据用于验证 trainset.targets = original_targets diff --git a/README.md b/README.md index 65c37050dc276a44176716a838f5a190c1fab0b4..ff6cc2d2cc7aaa103073b37bd94130317047610d 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ --- license: mit --- -# 模型训练过程汇总（持续更新中） +# 模型训练过程汇总[该仓库只含有image model的训练过程，其他类型模型只有训练代码] 本仓库采用扁平化的目录结构和标签系统来组织模型，具体说明如下： diff --git a/ttv_utils/save_embeddings.py b/ttv_utils/save_embeddings.py index be151dbf4b8e4a9bb8a02712909501e8dc9d94da..0f78154391036ed9c420ffb489e9a861e09950b3 100644 --- a/ttv_utils/save_embeddings.py +++ b/ttv_utils/save_embeddings.py @@ -16,7 +16,7 @@ class time_travel_saver: """ def __init__(self, model, dataloader, device, save_dir, model_name, interval=1, - auto_save_embedding=False, layer_name=None): + auto_save_embedding=False, layer_name=None,show = False): """初始化 Args: @@ -40,7 +40,67 @@ class time_travel_saver: self.current_epoch = 1 else: self.current_epoch = len(os.listdir(self.save_dir)) + + if show: + layer_dimensions = self.show_dimensions() + print(layer_dimensions) + + + def show_dimensions(self): + """显示模型中所有层的名称和对应的维度 + + 这个函数会输出模型中所有层的名称和它们的输出维度， + 帮助用户选择合适的层来提取特征。 + + Returns: + layer_dimensions: 包含层名称和维度的字典 + """ + activation = {} + layer_dimensions = {} + + def get_activation(name): + def hook(model, input, output): + activation[name] = output.detach() + return hook + + # 注册钩子到所有层 + handles = [] + for name, module in self.model.named_modules(): + if isinstance(module, nn.Module) and not isinstance(module, nn.ModuleList) and not isinstance(module, nn.ModuleDict): + handles.append(module.register_forward_hook(get_activation(name))) + + self.model.eval() + with torch.no_grad(): + # 获取一个batch来分析每层的输出维度 + inputs, _ = next(iter(self.dataloader)) + inputs = inputs.to(self.device) + _ = self.model(inputs) + + # 分析所有层的输出维度 + print("\n模型各层的名称和维度:") + print("-" * 50) + print(f"{'层名称':<40} {'特征维度':<15} {'输出形状'}") + print("-" * 50) + + for name, feat in activation.items(): + if feat is None: + continue + + # 获取特征维度（展平后） + feat_dim = feat.view(feat.size(0), -1).size(1) + layer_dimensions[name] = feat_dim + # 打印层信息 + shape_str = str(list(feat.shape)) + print(f"{name:<40} {feat_dim:<15} {shape_str}") + + print("-" * 50) + print("注: 特征维度是将输出张量展平后的维度大小") + + # 移除所有钩子 + for handle in handles: + handle.remove() + return layer_dimensions def _extract_features_and_predictions(self): """提取特征和预测结果