diff --git a/Image/MobileNetv3/code/backdoor_train.log b/Image/MobileNetv3/code/backdoor_train.log
deleted file mode 100644
index fa3d6b7dc41d2fafd934a8531cb15fbf009ee219..0000000000000000000000000000000000000000
--- a/Image/MobileNetv3/code/backdoor_train.log
+++ /dev/null
@@ -1,253 +0,0 @@
-2025-03-14 19:34:09,949 - train - INFO - 开始训练 mobilenetv3
-2025-03-14 19:34:09,950 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3
-2025-03-14 19:34:10,708 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.320 | Acc: 13.28%
-2025-03-14 19:34:13,447 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.146 | Acc: 21.54%
-2025-03-14 19:34:16,441 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 2.000 | Acc: 25.60%
-2025-03-14 19:34:19,272 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.921 | Acc: 28.29%
-2025-03-14 19:34:23,561 - train - INFO - Epoch: 1 | Test Loss: 1.631 | Test Acc: 40.55%
-2025-03-14 19:34:24,104 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.897 | Acc: 28.12%
-2025-03-14 19:34:26,866 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.680 | Acc: 36.49%
-2025-03-14 19:34:30,220 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.659 | Acc: 37.85%
-2025-03-14 19:34:33,127 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.647 | Acc: 38.59%
-2025-03-14 19:34:37,194 - train - INFO - Epoch: 2 | Test Loss: 1.613 | Test Acc: 42.79%
-2025-03-14 19:34:48,385 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.501 | Acc: 42.97%
-2025-03-14 19:34:51,567 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.587 | Acc: 40.91%
-2025-03-14 19:34:55,050 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.572 | Acc: 41.97%
-2025-03-14 19:35:00,286 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.556 | Acc: 42.79%
-2025-03-14 19:35:06,995 - train - INFO - Epoch: 3 | Test Loss: 1.529 | Test Acc: 43.45%
-2025-03-14 19:35:07,304 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.589 | Acc: 39.06%
-2025-03-14 19:35:10,973 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.471 | Acc: 46.25%
-2025-03-14 19:35:14,288 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.445 | Acc: 47.22%
-2025-03-14 19:35:17,798 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.425 | Acc: 48.28%
-2025-03-14 19:35:22,738 - train - INFO - Epoch: 4 | Test Loss: 1.349 | Test Acc: 51.18%
-2025-03-14 19:35:34,169 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.274 | Acc: 52.34%
-2025-03-14 19:35:37,141 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.370 | Acc: 51.04%
-2025-03-14 19:35:40,306 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.362 | Acc: 51.23%
-2025-03-14 19:35:43,178 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.360 | Acc: 51.26%
-2025-03-14 19:35:47,371 - train - INFO - Epoch: 5 | Test Loss: 1.291 | Test Acc: 54.39%
-2025-03-14 19:35:47,592 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.263 | Acc: 55.47%
-2025-03-14 19:35:50,454 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.312 | Acc: 52.95%
-2025-03-14 19:35:53,226 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.309 | Acc: 53.16%
-2025-03-14 19:35:55,871 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.297 | Acc: 53.53%
-2025-03-14 19:35:59,868 - train - INFO - Epoch: 6 | Test Loss: 1.257 | Test Acc: 54.75%
-2025-03-14 19:36:10,115 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.212 | Acc: 56.25%
-2025-03-14 19:36:13,079 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.261 | Acc: 54.90%
-2025-03-14 19:36:16,073 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.252 | Acc: 55.44%
-2025-03-14 19:36:18,985 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.249 | Acc: 55.56%
-2025-03-14 19:36:23,167 - train - INFO - Epoch: 7 | Test Loss: 1.328 | Test Acc: 51.79%
-2025-03-14 19:36:23,446 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.228 | Acc: 50.78%
-2025-03-14 19:36:26,398 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.213 | Acc: 57.07%
-2025-03-14 19:36:29,421 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.216 | Acc: 56.90%
-2025-03-14 19:36:32,300 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.214 | Acc: 56.96%
-2025-03-14 19:36:36,642 - train - INFO - Epoch: 8 | Test Loss: 1.222 | Test Acc: 55.97%
-2025-03-14 19:36:48,439 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.049 | Acc: 67.97%
-2025-03-14 19:36:51,749 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.208 | Acc: 56.95%
-2025-03-14 19:36:55,054 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.211 | Acc: 56.86%
-2025-03-14 19:36:58,052 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.210 | Acc: 57.06%
-2025-03-14 19:37:02,439 - train - INFO - Epoch: 9 | Test Loss: 1.255 | Test Acc: 54.03%
-2025-03-14 19:37:02,733 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.179 | Acc: 63.28%
-2025-03-14 19:37:06,315 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.209 | Acc: 57.66%
-2025-03-14 19:37:09,460 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.201 | Acc: 57.60%
-2025-03-14 19:37:12,816 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.202 | Acc: 57.50%
-2025-03-14 19:37:17,537 - train - INFO - Epoch: 10 | Test Loss: 1.179 | Test Acc: 57.09%
-2025-03-14 19:37:28,080 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.027 | Acc: 67.19%
-2025-03-14 19:37:30,927 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.176 | Acc: 58.61%
-2025-03-14 19:37:33,856 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.184 | Acc: 58.20%
-2025-03-14 19:37:36,622 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.180 | Acc: 58.40%
-2025-03-14 19:37:40,915 - train - INFO - Epoch: 11 | Test Loss: 1.203 | Test Acc: 57.33%
-2025-03-14 19:37:41,138 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.089 | Acc: 60.94%
-2025-03-14 19:37:44,042 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.183 | Acc: 58.00%
-2025-03-14 19:37:47,123 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.186 | Acc: 58.07%
-2025-03-14 19:37:50,181 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.186 | Acc: 58.21%
-2025-03-14 19:37:54,485 - train - INFO - Epoch: 12 | Test Loss: 1.184 | Test Acc: 58.28%
-2025-03-14 19:38:04,529 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.169 | Acc: 56.25%
-2025-03-14 19:38:07,411 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.180 | Acc: 58.57%
-2025-03-14 19:38:10,271 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.172 | Acc: 58.88%
-2025-03-14 19:38:13,588 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.170 | Acc: 58.83%
-2025-03-14 19:38:17,915 - train - INFO - Epoch: 13 | Test Loss: 1.277 | Test Acc: 55.18%
-2025-03-14 19:38:18,180 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.024 | Acc: 62.50%
-2025-03-14 19:38:21,018 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.148 | Acc: 59.14%
-2025-03-14 19:38:23,826 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.161 | Acc: 58.97%
-2025-03-14 19:38:26,656 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.164 | Acc: 58.82%
-2025-03-14 19:38:30,586 - train - INFO - Epoch: 14 | Test Loss: 1.276 | Test Acc: 55.02%
-2025-03-14 19:38:41,002 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 1.196 | Acc: 53.91%
-2025-03-14 19:38:43,862 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.157 | Acc: 59.38%
-2025-03-14 19:38:46,647 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.148 | Acc: 59.47%
-2025-03-14 19:38:49,537 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.150 | Acc: 59.41%
-2025-03-14 19:38:53,845 - train - INFO - Epoch: 15 | Test Loss: 1.164 | Test Acc: 59.17%
-2025-03-14 19:38:54,067 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 0.926 | Acc: 67.97%
-2025-03-14 19:38:56,970 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.142 | Acc: 59.78%
-2025-03-14 19:38:59,745 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.143 | Acc: 59.80%
-2025-03-14 19:39:02,600 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.146 | Acc: 59.79%
-2025-03-14 19:39:07,088 - train - INFO - Epoch: 16 | Test Loss: 1.145 | Test Acc: 59.72%
-2025-03-14 19:39:18,059 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 0.978 | Acc: 64.06%
-2025-03-14 19:39:21,062 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.159 | Acc: 59.11%
-2025-03-14 19:39:24,063 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.149 | Acc: 59.38%
-2025-03-14 19:39:27,180 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.145 | Acc: 59.82%
-2025-03-14 19:39:32,003 - train - INFO - Epoch: 17 | Test Loss: 1.164 | Test Acc: 59.60%
-2025-03-14 19:39:32,294 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.097 | Acc: 60.94%
-2025-03-14 19:39:35,379 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.131 | Acc: 59.47%
-2025-03-14 19:39:38,505 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.126 | Acc: 59.93%
-2025-03-14 19:39:41,838 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.124 | Acc: 60.26%
-2025-03-14 19:39:46,267 - train - INFO - Epoch: 18 | Test Loss: 1.178 | Test Acc: 58.70%
-2025-03-14 19:39:57,412 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.281 | Acc: 54.69%
-2025-03-14 19:40:00,334 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.131 | Acc: 60.60%
-2025-03-14 19:40:03,247 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.132 | Acc: 60.33%
-2025-03-14 19:40:06,091 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.131 | Acc: 60.40%
-2025-03-14 19:40:10,218 - train - INFO - Epoch: 19 | Test Loss: 1.146 | Test Acc: 59.70%
-2025-03-14 19:40:10,427 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.237 | Acc: 57.03%
-2025-03-14 19:40:13,329 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.105 | Acc: 61.37%
-2025-03-14 19:40:16,496 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.112 | Acc: 61.24%
-2025-03-14 19:40:19,346 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.119 | Acc: 61.04%
-2025-03-14 19:40:23,460 - train - INFO - Epoch: 20 | Test Loss: 1.178 | Test Acc: 58.22%
-2025-03-14 19:40:34,045 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.283 | Acc: 57.03%
-2025-03-14 19:40:36,967 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.125 | Acc: 60.55%
-2025-03-14 19:40:39,673 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.115 | Acc: 61.14%
-2025-03-14 19:40:42,896 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.119 | Acc: 60.80%
-2025-03-14 19:40:47,481 - train - INFO - Epoch: 21 | Test Loss: 1.148 | Test Acc: 58.85%
-2025-03-14 19:40:47,708 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 1.006 | Acc: 63.28%
-2025-03-14 19:40:50,647 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.109 | Acc: 61.04%
-2025-03-14 19:40:53,390 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.121 | Acc: 60.61%
-2025-03-14 19:40:56,265 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.121 | Acc: 60.59%
-2025-03-14 19:41:00,300 - train - INFO - Epoch: 22 | Test Loss: 1.252 | Test Acc: 56.00%
-2025-03-14 19:41:10,713 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.183 | Acc: 64.06%
-2025-03-14 19:41:13,642 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.141 | Acc: 60.41%
-2025-03-14 19:41:16,447 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.126 | Acc: 60.60%
-2025-03-14 19:41:19,466 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.126 | Acc: 60.61%
-2025-03-14 19:41:23,677 - train - INFO - Epoch: 23 | Test Loss: 1.355 | Test Acc: 53.98%
-2025-03-14 19:41:23,909 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.204 | Acc: 57.03%
-2025-03-14 19:41:26,799 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.111 | Acc: 60.94%
-2025-03-14 19:41:29,837 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.111 | Acc: 61.03%
-2025-03-14 19:41:32,845 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.112 | Acc: 60.86%
-2025-03-14 19:41:37,135 - train - INFO - Epoch: 24 | Test Loss: 1.229 | Test Acc: 56.77%
-2025-03-14 19:41:49,373 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.142 | Acc: 60.16%
-2025-03-14 19:41:52,683 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.096 | Acc: 62.07%
-2025-03-14 19:41:55,781 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.106 | Acc: 61.37%
-2025-03-14 19:41:58,961 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.105 | Acc: 61.47%
-2025-03-14 19:42:03,822 - train - INFO - Epoch: 25 | Test Loss: 1.208 | Test Acc: 56.94%
-2025-03-14 19:42:04,048 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 0.874 | Acc: 65.62%
-2025-03-14 19:42:07,041 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.107 | Acc: 60.66%
-2025-03-14 19:42:09,754 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.108 | Acc: 60.90%
-2025-03-14 19:42:12,340 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.103 | Acc: 61.09%
-2025-03-14 19:42:16,280 - train - INFO - Epoch: 26 | Test Loss: 1.272 | Test Acc: 54.55%
-2025-03-14 19:42:26,726 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.055 | Acc: 63.28%
-2025-03-14 19:42:29,611 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.098 | Acc: 61.42%
-2025-03-14 19:42:32,450 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.101 | Acc: 61.61%
-2025-03-14 19:42:35,164 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.110 | Acc: 61.25%
-2025-03-14 19:42:38,982 - train - INFO - Epoch: 27 | Test Loss: 1.093 | Test Acc: 61.80%
-2025-03-14 19:42:39,189 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.191 | Acc: 61.72%
-2025-03-14 19:42:42,143 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.113 | Acc: 60.58%
-2025-03-14 19:42:45,181 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.105 | Acc: 60.91%
-2025-03-14 19:42:48,103 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.107 | Acc: 60.91%
-2025-03-14 19:42:52,248 - train - INFO - Epoch: 28 | Test Loss: 1.132 | Test Acc: 60.36%
-2025-03-14 19:43:02,145 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.005 | Acc: 69.53%
-2025-03-14 19:43:04,936 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.107 | Acc: 61.42%
-2025-03-14 19:43:07,909 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.100 | Acc: 61.62%
-2025-03-14 19:43:10,658 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.097 | Acc: 61.47%
-2025-03-14 19:43:14,707 - train - INFO - Epoch: 29 | Test Loss: 1.365 | Test Acc: 52.51%
-2025-03-14 19:43:14,980 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 1.134 | Acc: 56.25%
-2025-03-14 19:43:17,981 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.101 | Acc: 61.53%
-2025-03-14 19:43:20,945 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.097 | Acc: 61.55%
-2025-03-14 19:43:23,828 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.096 | Acc: 61.62%
-2025-03-14 19:43:27,912 - train - INFO - Epoch: 30 | Test Loss: 1.058 | Test Acc: 62.23%
-2025-03-14 19:43:37,857 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 1.111 | Acc: 64.06%
-2025-03-14 19:43:40,744 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.093 | Acc: 61.44%
-2025-03-14 19:43:43,590 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.095 | Acc: 61.63%
-2025-03-14 19:43:46,301 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.087 | Acc: 61.93%
-2025-03-14 19:43:50,386 - train - INFO - Epoch: 31 | Test Loss: 1.227 | Test Acc: 56.04%
-2025-03-14 19:43:50,594 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 0.883 | Acc: 70.31%
-2025-03-14 19:43:53,386 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.085 | Acc: 62.06%
-2025-03-14 19:43:56,058 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.093 | Acc: 61.87%
-2025-03-14 19:43:58,777 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.095 | Acc: 61.84%
-2025-03-14 19:44:02,708 - train - INFO - Epoch: 32 | Test Loss: 1.217 | Test Acc: 57.42%
-2025-03-14 19:44:12,615 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.165 | Acc: 60.16%
-2025-03-14 19:44:15,397 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.087 | Acc: 61.80%
-2025-03-14 19:44:18,183 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.092 | Acc: 61.66%
-2025-03-14 19:44:21,023 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.088 | Acc: 61.89%
-2025-03-14 19:44:25,133 - train - INFO - Epoch: 33 | Test Loss: 1.139 | Test Acc: 59.81%
-2025-03-14 19:44:25,337 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.119 | Acc: 57.81%
-2025-03-14 19:44:28,094 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.081 | Acc: 62.59%
-2025-03-14 19:44:30,793 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.084 | Acc: 62.30%
-2025-03-14 19:44:33,468 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.088 | Acc: 62.01%
-2025-03-14 19:44:37,502 - train - INFO - Epoch: 34 | Test Loss: 1.096 | Test Acc: 61.46%
-2025-03-14 19:44:47,538 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.280 | Acc: 52.34%
-2025-03-14 19:44:50,462 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.105 | Acc: 61.56%
-2025-03-14 19:44:53,242 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.098 | Acc: 61.76%
-2025-03-14 19:44:56,109 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.094 | Acc: 61.89%
-2025-03-14 19:45:00,063 - train - INFO - Epoch: 35 | Test Loss: 1.179 | Test Acc: 59.79%
-2025-03-14 19:45:00,274 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.913 | Acc: 69.53%
-2025-03-14 19:45:03,181 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.049 | Acc: 63.35%
-2025-03-14 19:45:06,116 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.066 | Acc: 62.64%
-2025-03-14 19:45:09,021 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.079 | Acc: 62.11%
-2025-03-14 19:45:13,026 - train - INFO - Epoch: 36 | Test Loss: 1.090 | Test Acc: 61.83%
-2025-03-14 19:45:23,209 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.131 | Acc: 61.72%
-2025-03-14 19:45:26,014 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.073 | Acc: 62.70%
-2025-03-14 19:45:28,845 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.083 | Acc: 62.25%
-2025-03-14 19:45:31,768 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.080 | Acc: 62.33%
-2025-03-14 19:45:35,879 - train - INFO - Epoch: 37 | Test Loss: 1.146 | Test Acc: 58.65%
-2025-03-14 19:45:36,091 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 1.102 | Acc: 56.25%
-2025-03-14 19:45:38,959 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.089 | Acc: 61.91%
-2025-03-14 19:45:41,799 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.082 | Acc: 62.14%
-2025-03-14 19:45:44,479 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.092 | Acc: 61.85%
-2025-03-14 19:45:48,389 - train - INFO - Epoch: 38 | Test Loss: 1.114 | Test Acc: 60.73%
-2025-03-14 19:45:58,325 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.011 | Acc: 57.81%
-2025-03-14 19:46:01,154 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.086 | Acc: 61.43%
-2025-03-14 19:46:04,045 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.083 | Acc: 61.72%
-2025-03-14 19:46:06,946 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.083 | Acc: 61.85%
-2025-03-14 19:46:10,932 - train - INFO - Epoch: 39 | Test Loss: 1.192 | Test Acc: 58.43%
-2025-03-14 19:46:11,177 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.080 | Acc: 61.72%
-2025-03-14 19:46:14,380 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.103 | Acc: 62.06%
-2025-03-14 19:46:17,468 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.086 | Acc: 62.45%
-2025-03-14 19:46:20,564 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.084 | Acc: 62.41%
-2025-03-14 19:46:24,461 - train - INFO - Epoch: 40 | Test Loss: 1.151 | Test Acc: 59.16%
-2025-03-14 19:46:34,040 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 1.150 | Acc: 64.84%
-2025-03-14 19:46:36,830 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.068 | Acc: 62.60%
-2025-03-14 19:46:39,527 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.075 | Acc: 62.34%
-2025-03-14 19:46:42,471 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.079 | Acc: 62.17%
-2025-03-14 19:46:46,291 - train - INFO - Epoch: 41 | Test Loss: 1.162 | Test Acc: 59.24%
-2025-03-14 19:46:46,502 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 1.153 | Acc: 60.16%
-2025-03-14 19:46:49,178 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.087 | Acc: 61.93%
-2025-03-14 19:46:51,859 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.092 | Acc: 62.05%
-2025-03-14 19:46:54,717 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.084 | Acc: 62.22%
-2025-03-14 19:46:58,764 - train - INFO - Epoch: 42 | Test Loss: 1.114 | Test Acc: 61.30%
-2025-03-14 19:47:09,055 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.999 | Acc: 66.41%
-2025-03-14 19:47:11,844 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.078 | Acc: 62.23%
-2025-03-14 19:47:14,711 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.077 | Acc: 62.01%
-2025-03-14 19:47:17,266 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.079 | Acc: 62.16%
-2025-03-14 19:47:21,134 - train - INFO - Epoch: 43 | Test Loss: 1.125 | Test Acc: 60.02%
-2025-03-14 19:47:21,365 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.118 | Acc: 61.72%
-2025-03-14 19:47:24,096 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.069 | Acc: 62.43%
-2025-03-14 19:47:26,802 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.062 | Acc: 63.04%
-2025-03-14 19:47:29,582 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.069 | Acc: 62.77%
-2025-03-14 19:47:33,767 - train - INFO - Epoch: 44 | Test Loss: 1.221 | Test Acc: 59.06%
-2025-03-14 19:47:45,660 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.279 | Acc: 53.91%
-2025-03-14 19:47:48,442 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.077 | Acc: 62.37%
-2025-03-14 19:47:51,274 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.082 | Acc: 62.27%
-2025-03-14 19:47:54,009 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.080 | Acc: 62.46%
-2025-03-14 19:47:58,034 - train - INFO - Epoch: 45 | Test Loss: 1.185 | Test Acc: 58.87%
-2025-03-14 19:47:58,263 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.007 | Acc: 67.19%
-2025-03-14 19:48:01,118 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.084 | Acc: 62.31%
-2025-03-14 19:48:03,842 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.073 | Acc: 62.87%
-2025-03-14 19:48:06,567 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.072 | Acc: 62.83%
-2025-03-14 19:48:10,468 - train - INFO - Epoch: 46 | Test Loss: 1.052 | Test Acc: 63.17%
-2025-03-14 19:48:20,201 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.105 | Acc: 57.81%
-2025-03-14 19:48:23,066 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.051 | Acc: 63.25%
-2025-03-14 19:48:25,806 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.057 | Acc: 63.08%
-2025-03-14 19:48:28,567 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.068 | Acc: 62.67%
-2025-03-14 19:48:32,526 - train - INFO - Epoch: 47 | Test Loss: 1.130 | Test Acc: 60.48%
-2025-03-14 19:48:32,747 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.123 | Acc: 54.69%
-2025-03-14 19:48:35,546 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.069 | Acc: 62.48%
-2025-03-14 19:48:38,451 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.066 | Acc: 62.38%
-2025-03-14 19:48:41,132 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.074 | Acc: 62.11%
-2025-03-14 19:48:44,988 - train - INFO - Epoch: 48 | Test Loss: 1.077 | Test Acc: 61.90%
-2025-03-14 19:48:54,351 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.970 | Acc: 63.28%
-2025-03-14 19:48:57,080 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.059 | Acc: 62.83%
-2025-03-14 19:49:00,086 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.075 | Acc: 62.46%
-2025-03-14 19:49:02,812 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.076 | Acc: 62.34%
-2025-03-14 19:49:06,711 - train - INFO - Epoch: 49 | Test Loss: 1.124 | Test Acc: 59.80%
-2025-03-14 19:49:06,901 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 1.099 | Acc: 64.06%
-2025-03-14 19:49:09,703 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.057 | Acc: 63.00%
-2025-03-14 19:49:12,554 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.066 | Acc: 62.73%
-2025-03-14 19:49:15,667 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.065 | Acc: 62.84%
-2025-03-14 19:49:20,123 - train - INFO - Epoch: 50 | Test Loss: 1.100 | Test Acc: 61.13%
-2025-03-14 19:49:31,057 - train - INFO - 训练完成!
diff --git a/Image/MobileNetv3/code/model.py b/Image/MobileNetv3/code/model.py
deleted file mode 100644
index a685d44cc3f6b78e5cb4f3d92bbdc5784645ba91..0000000000000000000000000000000000000000
--- a/Image/MobileNetv3/code/model.py
+++ /dev/null
@@ -1,263 +0,0 @@
-'''
-MobileNetV3 in PyTorch.
-
-论文: "Searching for MobileNetV3"
-参考: https://arxiv.org/abs/1905.02244
-
-主要特点：
-1. 引入基于NAS的网络架构搜索
-2. 使用改进的SE注意力机块
-3. 使用h-swish激活函数
-4. 重新设计了网络的最后几层
-5. 提供了Large和Small两个版本
-'''
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def get_activation(name):
-    '''获取激活函数
-    
-    Args:
-        name: 激活函数名称 ('relu' 或 'hardswish')
-    '''
-    if name == 'relu':
-        return nn.ReLU(inplace=True)
-    elif name == 'hardswish':
-        return nn.Hardswish(inplace=True)
-    else:
-        raise NotImplementedError
-
-
-class SEModule(nn.Module):
-    '''Squeeze-and-Excitation模块
-    
-    通过全局平均池化和两层全连接网络学习通道注意力权重
-    
-    Args:
-        channel: 输入通道数
-        reduction: 降维比例
-    '''
-    def __init__(self, channel, reduction=4):
-        super(SEModule, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Hardsigmoid(inplace=True)
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)  # squeeze
-        y = self.fc(y).view(b, c, 1, 1)  # excitation
-        return x * y.expand_as(x)  # scale
-
-
-class Bottleneck(nn.Module):
-    '''MobileNetV3 Bottleneck
-    
-    包含:
-    1. Expansion layer (1x1 conv)
-    2. Depthwise layer (3x3 or 5x5 depthwise conv)
-    3. SE module (optional)
-    4. Projection layer (1x1 conv)
-    
-    Args:
-        in_channels: 输入通道数
-        exp_channels: 扩展层通道数
-        out_channels: 输出通道数
-        kernel_size: 深度卷积核大小
-        stride: 步长
-        use_SE: 是否使用SE模块
-        activation: 激活函数类型
-        use_residual: 是否使用残差连接
-    '''
-    def __init__(self, in_channels, exp_channels, out_channels, kernel_size, 
-                 stride, use_SE, activation, use_residual=True):
-        super(Bottleneck, self).__init__()
-        self.use_residual = use_residual and stride == 1 and in_channels == out_channels
-        padding = (kernel_size - 1) // 2
-
-        layers = []
-        # Expansion layer
-        if exp_channels != in_channels:
-            layers.extend([
-                nn.Conv2d(in_channels, exp_channels, 1, bias=False),
-                nn.BatchNorm2d(exp_channels),
-                get_activation(activation)
-            ])
-        
-        # Depthwise conv
-        layers.extend([
-            nn.Conv2d(
-                exp_channels, exp_channels, kernel_size, 
-                stride, padding, groups=exp_channels, bias=False
-            ),
-            nn.BatchNorm2d(exp_channels),
-            get_activation(activation)
-        ])
-
-        # SE module
-        if use_SE:
-            layers.append(SEModule(exp_channels))
-
-        # Projection layer
-        layers.extend([
-            nn.Conv2d(exp_channels, out_channels, 1, bias=False),
-            nn.BatchNorm2d(out_channels)
-        ])
-
-        self.conv = nn.Sequential(*layers)
-
-    def forward(self, x):
-        if self.use_residual:
-            return x + self.conv(x)
-        else:
-            return self.conv(x)
-
-
-class MobileNetV3(nn.Module):
-    '''MobileNetV3网络
-    
-    Args:
-        num_classes: 分类数量
-        mode: 'large' 或 'small'，选择网络版本
-    '''
-    def __init__(self, num_classes=10, mode='small'):
-        super(MobileNetV3, self).__init__()
-        
-        if mode == 'large':
-            # MobileNetV3-Large架构
-            self.config = [
-                # k, exp, out, SE, activation, stride
-                [3, 16, 16, False, 'relu', 1],
-                [3, 64, 24, False, 'relu', 2],
-                [3, 72, 24, False, 'relu', 1],
-                [5, 72, 40, True, 'relu', 2],
-                [5, 120, 40, True, 'relu', 1],
-                [5, 120, 40, True, 'relu', 1],
-                [3, 240, 80, False, 'hardswish', 2],
-                [3, 200, 80, False, 'hardswish', 1],
-                [3, 184, 80, False, 'hardswish', 1],
-                [3, 184, 80, False, 'hardswish', 1],
-                [3, 480, 112, True, 'hardswish', 1],
-                [3, 672, 112, True, 'hardswish', 1],
-                [5, 672, 160, True, 'hardswish', 2],
-                [5, 960, 160, True, 'hardswish', 1],
-                [5, 960, 160, True, 'hardswish', 1],
-            ]
-            init_conv_out = 16
-            final_conv_out = 960
-        else:
-            # MobileNetV3-Small架构
-            self.config = [
-                # k, exp, out, SE, activation, stride
-                [3, 16, 16, True, 'relu', 2],
-                [3, 72, 24, False, 'relu', 2],
-                [3, 88, 24, False, 'relu', 1],
-                [5, 96, 40, True, 'hardswish', 2],
-                [5, 240, 40, True, 'hardswish', 1],
-                [5, 240, 40, True, 'hardswish', 1],
-                [5, 120, 48, True, 'hardswish', 1],
-                [5, 144, 48, True, 'hardswish', 1],
-                [5, 288, 96, True, 'hardswish', 2],
-                [5, 576, 96, True, 'hardswish', 1],
-                [5, 576, 96, True, 'hardswish', 1],
-            ]
-            init_conv_out = 16
-            final_conv_out = 576
-
-        # 第一层卷积
-        self.conv_stem = nn.Sequential(
-            nn.Conv2d(3, init_conv_out, 3, 2, 1, bias=False),
-            nn.BatchNorm2d(init_conv_out),
-            get_activation('hardswish')
-        )
-
-        # 构建Bottleneck层
-        features = []
-        in_channels = init_conv_out
-        for k, exp, out, se, activation, stride in self.config:
-            features.append(
-                Bottleneck(in_channels, exp, out, k, stride, se, activation)
-            )
-            in_channels = out
-        self.features = nn.Sequential(*features)
-
-        # 最后的卷积层
-        self.conv_head = nn.Sequential(
-            nn.Conv2d(in_channels, final_conv_out, 1, bias=False),
-            nn.BatchNorm2d(final_conv_out),
-            get_activation('hardswish')
-        )
-
-        # 分类器
-        self.avgpool = nn.AdaptiveAvgPool2d(1)
-        self.classifier = nn.Sequential(
-            nn.Linear(final_conv_out, num_classes)
-        )
-
-        # 初始化权重
-        self._initialize_weights()
-
-    def _initialize_weights(self):
-        '''初始化模型权重'''
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out')
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.ones_(m.weight)
-                nn.init.zeros_(m.bias)
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-
-    def forward(self, x):
-        x = self.conv_stem(x)
-        x = self.features(x)
-        x = self.conv_head(x)
-        x = self.avgpool(x)
-        x = x.view(x.size(0), -1)
-        x = self.classifier(x)
-        return x
-
-    def feature(self, x):
-        x = self.conv_stem(x)
-        x = self.features(x)
-        x = self.conv_head(x)
-        x = self.avgpool(x)
-        return x
-
-    def prediction(self, x):
-        x = x.view(x.size(0), -1)
-        x = self.classifier(x)
-        return x
-        
-def test():
-    """测试函数"""
-    # 测试Large版本
-    net_large = MobileNetV3(mode='large')
-    x = torch.randn(2, 3, 32, 32)
-    y = net_large(x)
-    print('Large output size:', y.size())
-    
-    # 测试Small版本
-    net_small = MobileNetV3(mode='small')
-    y = net_small(x)
-    print('Small output size:', y.size())
-    
-    # 打印模型结构
-    from torchinfo import summary
-    device = 'cuda' if torch.cuda.is_available() else 'cpu'
-    net_small = net_small.to(device)
-    summary(net_small, (2, 3, 32, 32))
-
-if __name__ == '__main__':
-    test()
diff --git a/Image/MobileNetv3/code/train.log b/Image/MobileNetv3/code/train.log
deleted file mode 100644
index 71c61fea13a3db19cb353f9e6bb6a9754a4ecae3..0000000000000000000000000000000000000000
--- a/Image/MobileNetv3/code/train.log
+++ /dev/null
@@ -1,253 +0,0 @@
-2025-03-14 19:34:03,811 - train - INFO - 开始训练 mobilenetv3
-2025-03-14 19:34:03,812 - train - INFO - 总轮数: 50, 学习率: 0.1, 设备: cuda:3
-2025-03-14 19:34:04,510 - train - INFO - Epoch: 1 | Batch: 0 | Loss: 2.315 | Acc: 8.59%
-2025-03-14 19:34:07,447 - train - INFO - Epoch: 1 | Batch: 100 | Loss: 2.150 | Acc: 21.51%
-2025-03-14 19:34:10,386 - train - INFO - Epoch: 1 | Batch: 200 | Loss: 1.973 | Acc: 26.37%
-2025-03-14 19:34:13,121 - train - INFO - Epoch: 1 | Batch: 300 | Loss: 1.881 | Acc: 29.73%
-2025-03-14 19:34:17,285 - train - INFO - Epoch: 1 | Test Loss: 1.600 | Test Acc: 40.59%
-2025-03-14 19:34:17,773 - train - INFO - Epoch: 2 | Batch: 0 | Loss: 1.550 | Acc: 39.06%
-2025-03-14 19:34:20,584 - train - INFO - Epoch: 2 | Batch: 100 | Loss: 1.586 | Acc: 40.99%
-2025-03-14 19:34:23,574 - train - INFO - Epoch: 2 | Batch: 200 | Loss: 1.574 | Acc: 41.84%
-2025-03-14 19:34:26,466 - train - INFO - Epoch: 2 | Batch: 300 | Loss: 1.556 | Acc: 42.67%
-2025-03-14 19:34:31,001 - train - INFO - Epoch: 2 | Test Loss: 1.407 | Test Acc: 48.43%
-2025-03-14 19:34:41,695 - train - INFO - Epoch: 3 | Batch: 0 | Loss: 1.286 | Acc: 54.69%
-2025-03-14 19:34:44,711 - train - INFO - Epoch: 3 | Batch: 100 | Loss: 1.447 | Acc: 47.37%
-2025-03-14 19:34:47,946 - train - INFO - Epoch: 3 | Batch: 200 | Loss: 1.443 | Acc: 47.33%
-2025-03-14 19:34:50,944 - train - INFO - Epoch: 3 | Batch: 300 | Loss: 1.441 | Acc: 47.48%
-2025-03-14 19:34:56,768 - train - INFO - Epoch: 3 | Test Loss: 1.348 | Test Acc: 51.13%
-2025-03-14 19:34:57,073 - train - INFO - Epoch: 4 | Batch: 0 | Loss: 1.300 | Acc: 57.03%
-2025-03-14 19:35:02,274 - train - INFO - Epoch: 4 | Batch: 100 | Loss: 1.380 | Acc: 49.33%
-2025-03-14 19:35:06,559 - train - INFO - Epoch: 4 | Batch: 200 | Loss: 1.368 | Acc: 50.15%
-2025-03-14 19:35:10,317 - train - INFO - Epoch: 4 | Batch: 300 | Loss: 1.362 | Acc: 50.43%
-2025-03-14 19:35:15,273 - train - INFO - Epoch: 4 | Test Loss: 1.359 | Test Acc: 50.22%
-2025-03-14 19:35:28,028 - train - INFO - Epoch: 5 | Batch: 0 | Loss: 1.331 | Acc: 52.34%
-2025-03-14 19:35:31,190 - train - INFO - Epoch: 5 | Batch: 100 | Loss: 1.320 | Acc: 52.00%
-2025-03-14 19:35:34,388 - train - INFO - Epoch: 5 | Batch: 200 | Loss: 1.321 | Acc: 51.92%
-2025-03-14 19:35:37,466 - train - INFO - Epoch: 5 | Batch: 300 | Loss: 1.320 | Acc: 51.83%
-2025-03-14 19:35:41,929 - train - INFO - Epoch: 5 | Test Loss: 1.392 | Test Acc: 47.65%
-2025-03-14 19:35:42,179 - train - INFO - Epoch: 6 | Batch: 0 | Loss: 1.329 | Acc: 47.66%
-2025-03-14 19:35:45,106 - train - INFO - Epoch: 6 | Batch: 100 | Loss: 1.295 | Acc: 53.03%
-2025-03-14 19:35:48,220 - train - INFO - Epoch: 6 | Batch: 200 | Loss: 1.293 | Acc: 53.54%
-2025-03-14 19:35:51,284 - train - INFO - Epoch: 6 | Batch: 300 | Loss: 1.287 | Acc: 53.52%
-2025-03-14 19:35:55,377 - train - INFO - Epoch: 6 | Test Loss: 1.222 | Test Acc: 56.24%
-2025-03-14 19:36:05,521 - train - INFO - Epoch: 7 | Batch: 0 | Loss: 1.276 | Acc: 55.47%
-2025-03-14 19:36:08,675 - train - INFO - Epoch: 7 | Batch: 100 | Loss: 1.254 | Acc: 54.59%
-2025-03-14 19:36:11,596 - train - INFO - Epoch: 7 | Batch: 200 | Loss: 1.253 | Acc: 54.56%
-2025-03-14 19:36:14,501 - train - INFO - Epoch: 7 | Batch: 300 | Loss: 1.252 | Acc: 54.65%
-2025-03-14 19:36:18,647 - train - INFO - Epoch: 7 | Test Loss: 1.287 | Test Acc: 53.84%
-2025-03-14 19:36:18,846 - train - INFO - Epoch: 8 | Batch: 0 | Loss: 1.139 | Acc: 53.91%
-2025-03-14 19:36:21,672 - train - INFO - Epoch: 8 | Batch: 100 | Loss: 1.219 | Acc: 56.05%
-2025-03-14 19:36:24,552 - train - INFO - Epoch: 8 | Batch: 200 | Loss: 1.222 | Acc: 55.93%
-2025-03-14 19:36:27,453 - train - INFO - Epoch: 8 | Batch: 300 | Loss: 1.219 | Acc: 56.18%
-2025-03-14 19:36:31,835 - train - INFO - Epoch: 8 | Test Loss: 1.185 | Test Acc: 56.67%
-2025-03-14 19:36:43,478 - train - INFO - Epoch: 9 | Batch: 0 | Loss: 1.177 | Acc: 63.28%
-2025-03-14 19:36:46,534 - train - INFO - Epoch: 9 | Batch: 100 | Loss: 1.201 | Acc: 56.69%
-2025-03-14 19:36:49,936 - train - INFO - Epoch: 9 | Batch: 200 | Loss: 1.210 | Acc: 56.46%
-2025-03-14 19:36:52,915 - train - INFO - Epoch: 9 | Batch: 300 | Loss: 1.212 | Acc: 56.35%
-2025-03-14 19:36:57,694 - train - INFO - Epoch: 9 | Test Loss: 1.222 | Test Acc: 55.79%
-2025-03-14 19:36:57,918 - train - INFO - Epoch: 10 | Batch: 0 | Loss: 1.059 | Acc: 64.84%
-2025-03-14 19:37:00,868 - train - INFO - Epoch: 10 | Batch: 100 | Loss: 1.213 | Acc: 56.59%
-2025-03-14 19:37:04,043 - train - INFO - Epoch: 10 | Batch: 200 | Loss: 1.205 | Acc: 56.68%
-2025-03-14 19:37:07,484 - train - INFO - Epoch: 10 | Batch: 300 | Loss: 1.204 | Acc: 56.67%
-2025-03-14 19:37:12,223 - train - INFO - Epoch: 10 | Test Loss: 1.201 | Test Acc: 56.86%
-2025-03-14 19:37:23,335 - train - INFO - Epoch: 11 | Batch: 0 | Loss: 1.118 | Acc: 56.25%
-2025-03-14 19:37:26,292 - train - INFO - Epoch: 11 | Batch: 100 | Loss: 1.184 | Acc: 57.36%
-2025-03-14 19:37:29,243 - train - INFO - Epoch: 11 | Batch: 200 | Loss: 1.170 | Acc: 57.91%
-2025-03-14 19:37:32,097 - train - INFO - Epoch: 11 | Batch: 300 | Loss: 1.178 | Acc: 57.66%
-2025-03-14 19:37:36,266 - train - INFO - Epoch: 11 | Test Loss: 1.338 | Test Acc: 52.61%
-2025-03-14 19:37:36,479 - train - INFO - Epoch: 12 | Batch: 0 | Loss: 1.215 | Acc: 50.78%
-2025-03-14 19:37:39,302 - train - INFO - Epoch: 12 | Batch: 100 | Loss: 1.181 | Acc: 57.33%
-2025-03-14 19:37:42,060 - train - INFO - Epoch: 12 | Batch: 200 | Loss: 1.187 | Acc: 57.51%
-2025-03-14 19:37:44,805 - train - INFO - Epoch: 12 | Batch: 300 | Loss: 1.177 | Acc: 57.82%
-2025-03-14 19:37:49,023 - train - INFO - Epoch: 12 | Test Loss: 1.194 | Test Acc: 56.89%
-2025-03-14 19:37:59,417 - train - INFO - Epoch: 13 | Batch: 0 | Loss: 1.232 | Acc: 53.12%
-2025-03-14 19:38:02,245 - train - INFO - Epoch: 13 | Batch: 100 | Loss: 1.158 | Acc: 58.08%
-2025-03-14 19:38:05,015 - train - INFO - Epoch: 13 | Batch: 200 | Loss: 1.163 | Acc: 58.04%
-2025-03-14 19:38:07,820 - train - INFO - Epoch: 13 | Batch: 300 | Loss: 1.159 | Acc: 58.26%
-2025-03-14 19:38:11,959 - train - INFO - Epoch: 13 | Test Loss: 1.149 | Test Acc: 58.96%
-2025-03-14 19:38:12,197 - train - INFO - Epoch: 14 | Batch: 0 | Loss: 1.201 | Acc: 57.03%
-2025-03-14 19:38:15,304 - train - INFO - Epoch: 14 | Batch: 100 | Loss: 1.139 | Acc: 59.60%
-2025-03-14 19:38:18,315 - train - INFO - Epoch: 14 | Batch: 200 | Loss: 1.154 | Acc: 58.94%
-2025-03-14 19:38:21,155 - train - INFO - Epoch: 14 | Batch: 300 | Loss: 1.153 | Acc: 58.77%
-2025-03-14 19:38:25,190 - train - INFO - Epoch: 14 | Test Loss: 1.138 | Test Acc: 59.41%
-2025-03-14 19:38:35,745 - train - INFO - Epoch: 15 | Batch: 0 | Loss: 0.976 | Acc: 66.41%
-2025-03-14 19:38:38,693 - train - INFO - Epoch: 15 | Batch: 100 | Loss: 1.149 | Acc: 59.11%
-2025-03-14 19:38:41,527 - train - INFO - Epoch: 15 | Batch: 200 | Loss: 1.141 | Acc: 59.21%
-2025-03-14 19:38:44,307 - train - INFO - Epoch: 15 | Batch: 300 | Loss: 1.145 | Acc: 59.00%
-2025-03-14 19:38:48,357 - train - INFO - Epoch: 15 | Test Loss: 1.068 | Test Acc: 62.03%
-2025-03-14 19:38:48,571 - train - INFO - Epoch: 16 | Batch: 0 | Loss: 1.218 | Acc: 53.12%
-2025-03-14 19:38:51,535 - train - INFO - Epoch: 16 | Batch: 100 | Loss: 1.121 | Acc: 59.31%
-2025-03-14 19:38:54,473 - train - INFO - Epoch: 16 | Batch: 200 | Loss: 1.124 | Acc: 59.33%
-2025-03-14 19:38:57,299 - train - INFO - Epoch: 16 | Batch: 300 | Loss: 1.128 | Acc: 59.45%
-2025-03-14 19:39:01,582 - train - INFO - Epoch: 16 | Test Loss: 1.117 | Test Acc: 60.35%
-2025-03-14 19:39:12,941 - train - INFO - Epoch: 17 | Batch: 0 | Loss: 1.216 | Acc: 60.16%
-2025-03-14 19:39:15,823 - train - INFO - Epoch: 17 | Batch: 100 | Loss: 1.128 | Acc: 59.86%
-2025-03-14 19:39:18,876 - train - INFO - Epoch: 17 | Batch: 200 | Loss: 1.124 | Acc: 59.88%
-2025-03-14 19:39:21,926 - train - INFO - Epoch: 17 | Batch: 300 | Loss: 1.131 | Acc: 59.47%
-2025-03-14 19:39:26,176 - train - INFO - Epoch: 17 | Test Loss: 1.115 | Test Acc: 60.26%
-2025-03-14 19:39:26,410 - train - INFO - Epoch: 18 | Batch: 0 | Loss: 1.197 | Acc: 54.69%
-2025-03-14 19:39:29,699 - train - INFO - Epoch: 18 | Batch: 100 | Loss: 1.118 | Acc: 60.14%
-2025-03-14 19:39:33,109 - train - INFO - Epoch: 18 | Batch: 200 | Loss: 1.124 | Acc: 59.79%
-2025-03-14 19:39:36,272 - train - INFO - Epoch: 18 | Batch: 300 | Loss: 1.131 | Acc: 59.71%
-2025-03-14 19:39:40,820 - train - INFO - Epoch: 18 | Test Loss: 1.250 | Test Acc: 55.97%
-2025-03-14 19:39:51,937 - train - INFO - Epoch: 19 | Batch: 0 | Loss: 1.201 | Acc: 52.34%
-2025-03-14 19:39:55,019 - train - INFO - Epoch: 19 | Batch: 100 | Loss: 1.120 | Acc: 60.21%
-2025-03-14 19:39:57,784 - train - INFO - Epoch: 19 | Batch: 200 | Loss: 1.121 | Acc: 60.15%
-2025-03-14 19:40:00,471 - train - INFO - Epoch: 19 | Batch: 300 | Loss: 1.119 | Acc: 60.19%
-2025-03-14 19:40:04,498 - train - INFO - Epoch: 19 | Test Loss: 1.198 | Test Acc: 57.94%
-2025-03-14 19:40:04,737 - train - INFO - Epoch: 20 | Batch: 0 | Loss: 1.173 | Acc: 59.38%
-2025-03-14 19:40:07,499 - train - INFO - Epoch: 20 | Batch: 100 | Loss: 1.108 | Acc: 60.23%
-2025-03-14 19:40:10,339 - train - INFO - Epoch: 20 | Batch: 200 | Loss: 1.119 | Acc: 60.20%
-2025-03-14 19:40:13,119 - train - INFO - Epoch: 20 | Batch: 300 | Loss: 1.112 | Acc: 60.39%
-2025-03-14 19:40:17,467 - train - INFO - Epoch: 20 | Test Loss: 1.308 | Test Acc: 54.57%
-2025-03-14 19:40:27,692 - train - INFO - Epoch: 21 | Batch: 0 | Loss: 1.066 | Acc: 59.38%
-2025-03-14 19:40:30,553 - train - INFO - Epoch: 21 | Batch: 100 | Loss: 1.133 | Acc: 59.80%
-2025-03-14 19:40:33,490 - train - INFO - Epoch: 21 | Batch: 200 | Loss: 1.120 | Acc: 60.18%
-2025-03-14 19:40:36,428 - train - INFO - Epoch: 21 | Batch: 300 | Loss: 1.118 | Acc: 60.36%
-2025-03-14 19:40:40,344 - train - INFO - Epoch: 21 | Test Loss: 1.159 | Test Acc: 58.64%
-2025-03-14 19:40:40,605 - train - INFO - Epoch: 22 | Batch: 0 | Loss: 0.997 | Acc: 63.28%
-2025-03-14 19:40:44,155 - train - INFO - Epoch: 22 | Batch: 100 | Loss: 1.100 | Acc: 60.76%
-2025-03-14 19:40:47,367 - train - INFO - Epoch: 22 | Batch: 200 | Loss: 1.096 | Acc: 60.92%
-2025-03-14 19:40:50,149 - train - INFO - Epoch: 22 | Batch: 300 | Loss: 1.099 | Acc: 60.99%
-2025-03-14 19:40:54,308 - train - INFO - Epoch: 22 | Test Loss: 1.089 | Test Acc: 61.60%
-2025-03-14 19:41:04,532 - train - INFO - Epoch: 23 | Batch: 0 | Loss: 1.029 | Acc: 66.41%
-2025-03-14 19:41:07,395 - train - INFO - Epoch: 23 | Batch: 100 | Loss: 1.086 | Acc: 61.11%
-2025-03-14 19:41:10,298 - train - INFO - Epoch: 23 | Batch: 200 | Loss: 1.086 | Acc: 61.38%
-2025-03-14 19:41:13,267 - train - INFO - Epoch: 23 | Batch: 300 | Loss: 1.091 | Acc: 61.09%
-2025-03-14 19:41:17,403 - train - INFO - Epoch: 23 | Test Loss: 1.116 | Test Acc: 60.21%
-2025-03-14 19:41:17,641 - train - INFO - Epoch: 24 | Batch: 0 | Loss: 1.143 | Acc: 66.41%
-2025-03-14 19:41:20,685 - train - INFO - Epoch: 24 | Batch: 100 | Loss: 1.091 | Acc: 61.22%
-2025-03-14 19:41:23,621 - train - INFO - Epoch: 24 | Batch: 200 | Loss: 1.091 | Acc: 61.34%
-2025-03-14 19:41:26,610 - train - INFO - Epoch: 24 | Batch: 300 | Loss: 1.092 | Acc: 61.26%
-2025-03-14 19:41:30,833 - train - INFO - Epoch: 24 | Test Loss: 1.095 | Test Acc: 61.01%
-2025-03-14 19:41:42,216 - train - INFO - Epoch: 25 | Batch: 0 | Loss: 1.079 | Acc: 64.84%
-2025-03-14 19:41:45,275 - train - INFO - Epoch: 25 | Batch: 100 | Loss: 1.082 | Acc: 61.22%
-2025-03-14 19:41:48,865 - train - INFO - Epoch: 25 | Batch: 200 | Loss: 1.084 | Acc: 61.43%
-2025-03-14 19:41:52,343 - train - INFO - Epoch: 25 | Batch: 300 | Loss: 1.078 | Acc: 61.63%
-2025-03-14 19:41:56,861 - train - INFO - Epoch: 25 | Test Loss: 1.052 | Test Acc: 61.88%
-2025-03-14 19:41:57,122 - train - INFO - Epoch: 26 | Batch: 0 | Loss: 1.047 | Acc: 63.28%
-2025-03-14 19:42:00,289 - train - INFO - Epoch: 26 | Batch: 100 | Loss: 1.087 | Acc: 61.63%
-2025-03-14 19:42:03,526 - train - INFO - Epoch: 26 | Batch: 200 | Loss: 1.087 | Acc: 61.79%
-2025-03-14 19:42:06,632 - train - INFO - Epoch: 26 | Batch: 300 | Loss: 1.083 | Acc: 61.67%
-2025-03-14 19:42:10,746 - train - INFO - Epoch: 26 | Test Loss: 1.086 | Test Acc: 61.65%
-2025-03-14 19:42:20,622 - train - INFO - Epoch: 27 | Batch: 0 | Loss: 1.134 | Acc: 61.72%
-2025-03-14 19:42:23,602 - train - INFO - Epoch: 27 | Batch: 100 | Loss: 1.076 | Acc: 61.42%
-2025-03-14 19:42:26,568 - train - INFO - Epoch: 27 | Batch: 200 | Loss: 1.072 | Acc: 61.75%
-2025-03-14 19:42:29,502 - train - INFO - Epoch: 27 | Batch: 300 | Loss: 1.079 | Acc: 61.47%
-2025-03-14 19:42:33,566 - train - INFO - Epoch: 27 | Test Loss: 1.135 | Test Acc: 60.40%
-2025-03-14 19:42:33,802 - train - INFO - Epoch: 28 | Batch: 0 | Loss: 1.246 | Acc: 50.00%
-2025-03-14 19:42:36,640 - train - INFO - Epoch: 28 | Batch: 100 | Loss: 1.069 | Acc: 62.02%
-2025-03-14 19:42:39,573 - train - INFO - Epoch: 28 | Batch: 200 | Loss: 1.075 | Acc: 61.68%
-2025-03-14 19:42:42,773 - train - INFO - Epoch: 28 | Batch: 300 | Loss: 1.077 | Acc: 61.52%
-2025-03-14 19:42:46,877 - train - INFO - Epoch: 28 | Test Loss: 1.093 | Test Acc: 61.21%
-2025-03-14 19:42:56,985 - train - INFO - Epoch: 29 | Batch: 0 | Loss: 1.170 | Acc: 57.03%
-2025-03-14 19:42:59,789 - train - INFO - Epoch: 29 | Batch: 100 | Loss: 1.070 | Acc: 62.21%
-2025-03-14 19:43:02,512 - train - INFO - Epoch: 29 | Batch: 200 | Loss: 1.067 | Acc: 62.04%
-2025-03-14 19:43:05,191 - train - INFO - Epoch: 29 | Batch: 300 | Loss: 1.068 | Acc: 61.89%
-2025-03-14 19:43:09,441 - train - INFO - Epoch: 29 | Test Loss: 1.108 | Test Acc: 61.02%
-2025-03-14 19:43:09,674 - train - INFO - Epoch: 30 | Batch: 0 | Loss: 0.939 | Acc: 67.19%
-2025-03-14 19:43:12,474 - train - INFO - Epoch: 30 | Batch: 100 | Loss: 1.060 | Acc: 61.67%
-2025-03-14 19:43:15,323 - train - INFO - Epoch: 30 | Batch: 200 | Loss: 1.067 | Acc: 61.65%
-2025-03-14 19:43:18,162 - train - INFO - Epoch: 30 | Batch: 300 | Loss: 1.060 | Acc: 62.02%
-2025-03-14 19:43:22,287 - train - INFO - Epoch: 30 | Test Loss: 1.144 | Test Acc: 60.34%
-2025-03-14 19:43:32,512 - train - INFO - Epoch: 31 | Batch: 0 | Loss: 0.962 | Acc: 65.62%
-2025-03-14 19:43:35,242 - train - INFO - Epoch: 31 | Batch: 100 | Loss: 1.066 | Acc: 61.82%
-2025-03-14 19:43:37,851 - train - INFO - Epoch: 31 | Batch: 200 | Loss: 1.071 | Acc: 61.85%
-2025-03-14 19:43:40,703 - train - INFO - Epoch: 31 | Batch: 300 | Loss: 1.067 | Acc: 61.91%
-2025-03-14 19:43:44,687 - train - INFO - Epoch: 31 | Test Loss: 1.087 | Test Acc: 61.81%
-2025-03-14 19:43:44,939 - train - INFO - Epoch: 32 | Batch: 0 | Loss: 1.026 | Acc: 60.16%
-2025-03-14 19:43:47,798 - train - INFO - Epoch: 32 | Batch: 100 | Loss: 1.075 | Acc: 61.95%
-2025-03-14 19:43:50,802 - train - INFO - Epoch: 32 | Batch: 200 | Loss: 1.061 | Acc: 62.41%
-2025-03-14 19:43:53,659 - train - INFO - Epoch: 32 | Batch: 300 | Loss: 1.064 | Acc: 62.34%
-2025-03-14 19:43:57,726 - train - INFO - Epoch: 32 | Test Loss: 1.134 | Test Acc: 60.53%
-2025-03-14 19:44:07,467 - train - INFO - Epoch: 33 | Batch: 0 | Loss: 1.038 | Acc: 67.97%
-2025-03-14 19:44:10,465 - train - INFO - Epoch: 33 | Batch: 100 | Loss: 1.072 | Acc: 61.79%
-2025-03-14 19:44:13,127 - train - INFO - Epoch: 33 | Batch: 200 | Loss: 1.060 | Acc: 62.29%
-2025-03-14 19:44:15,841 - train - INFO - Epoch: 33 | Batch: 300 | Loss: 1.064 | Acc: 62.27%
-2025-03-14 19:44:19,860 - train - INFO - Epoch: 33 | Test Loss: 1.025 | Test Acc: 63.50%
-2025-03-14 19:44:20,077 - train - INFO - Epoch: 34 | Batch: 0 | Loss: 1.182 | Acc: 58.59%
-2025-03-14 19:44:23,002 - train - INFO - Epoch: 34 | Batch: 100 | Loss: 1.041 | Acc: 62.76%
-2025-03-14 19:44:26,161 - train - INFO - Epoch: 34 | Batch: 200 | Loss: 1.044 | Acc: 62.88%
-2025-03-14 19:44:29,066 - train - INFO - Epoch: 34 | Batch: 300 | Loss: 1.052 | Acc: 62.61%
-2025-03-14 19:44:32,974 - train - INFO - Epoch: 34 | Test Loss: 1.165 | Test Acc: 59.01%
-2025-03-14 19:44:43,069 - train - INFO - Epoch: 35 | Batch: 0 | Loss: 1.273 | Acc: 57.03%
-2025-03-14 19:44:45,817 - train - INFO - Epoch: 35 | Batch: 100 | Loss: 1.028 | Acc: 63.61%
-2025-03-14 19:44:48,611 - train - INFO - Epoch: 35 | Batch: 200 | Loss: 1.047 | Acc: 63.04%
-2025-03-14 19:44:51,395 - train - INFO - Epoch: 35 | Batch: 300 | Loss: 1.053 | Acc: 62.75%
-2025-03-14 19:44:55,612 - train - INFO - Epoch: 35 | Test Loss: 1.135 | Test Acc: 60.02%
-2025-03-14 19:44:55,809 - train - INFO - Epoch: 36 | Batch: 0 | Loss: 0.908 | Acc: 67.97%
-2025-03-14 19:44:58,620 - train - INFO - Epoch: 36 | Batch: 100 | Loss: 1.051 | Acc: 62.64%
-2025-03-14 19:45:01,440 - train - INFO - Epoch: 36 | Batch: 200 | Loss: 1.051 | Acc: 62.66%
-2025-03-14 19:45:04,528 - train - INFO - Epoch: 36 | Batch: 300 | Loss: 1.053 | Acc: 62.72%
-2025-03-14 19:45:08,941 - train - INFO - Epoch: 36 | Test Loss: 1.187 | Test Acc: 57.95%
-2025-03-14 19:45:18,855 - train - INFO - Epoch: 37 | Batch: 0 | Loss: 1.220 | Acc: 55.47%
-2025-03-14 19:45:21,638 - train - INFO - Epoch: 37 | Batch: 100 | Loss: 1.021 | Acc: 63.85%
-2025-03-14 19:45:24,446 - train - INFO - Epoch: 37 | Batch: 200 | Loss: 1.041 | Acc: 63.18%
-2025-03-14 19:45:27,235 - train - INFO - Epoch: 37 | Batch: 300 | Loss: 1.036 | Acc: 63.54%
-2025-03-14 19:45:31,429 - train - INFO - Epoch: 37 | Test Loss: 1.055 | Test Acc: 62.86%
-2025-03-14 19:45:31,653 - train - INFO - Epoch: 38 | Batch: 0 | Loss: 0.931 | Acc: 62.50%
-2025-03-14 19:45:34,563 - train - INFO - Epoch: 38 | Batch: 100 | Loss: 1.018 | Acc: 63.77%
-2025-03-14 19:45:37,461 - train - INFO - Epoch: 38 | Batch: 200 | Loss: 1.037 | Acc: 63.12%
-2025-03-14 19:45:40,390 - train - INFO - Epoch: 38 | Batch: 300 | Loss: 1.049 | Acc: 62.78%
-2025-03-14 19:45:44,376 - train - INFO - Epoch: 38 | Test Loss: 1.055 | Test Acc: 61.96%
-2025-03-14 19:45:54,849 - train - INFO - Epoch: 39 | Batch: 0 | Loss: 1.256 | Acc: 60.94%
-2025-03-14 19:45:57,611 - train - INFO - Epoch: 39 | Batch: 100 | Loss: 1.041 | Acc: 63.05%
-2025-03-14 19:46:00,366 - train - INFO - Epoch: 39 | Batch: 200 | Loss: 1.043 | Acc: 62.62%
-2025-03-14 19:46:03,308 - train - INFO - Epoch: 39 | Batch: 300 | Loss: 1.040 | Acc: 62.69%
-2025-03-14 19:46:07,524 - train - INFO - Epoch: 39 | Test Loss: 1.217 | Test Acc: 58.12%
-2025-03-14 19:46:07,739 - train - INFO - Epoch: 40 | Batch: 0 | Loss: 1.104 | Acc: 60.94%
-2025-03-14 19:46:10,580 - train - INFO - Epoch: 40 | Batch: 100 | Loss: 1.027 | Acc: 63.51%
-2025-03-14 19:46:13,745 - train - INFO - Epoch: 40 | Batch: 200 | Loss: 1.030 | Acc: 63.62%
-2025-03-14 19:46:16,973 - train - INFO - Epoch: 40 | Batch: 300 | Loss: 1.042 | Acc: 63.13%
-2025-03-14 19:46:21,340 - train - INFO - Epoch: 40 | Test Loss: 1.219 | Test Acc: 58.91%
-2025-03-14 19:46:30,937 - train - INFO - Epoch: 41 | Batch: 0 | Loss: 0.936 | Acc: 65.62%
-2025-03-14 19:46:33,767 - train - INFO - Epoch: 41 | Batch: 100 | Loss: 1.036 | Acc: 63.67%
-2025-03-14 19:46:36,441 - train - INFO - Epoch: 41 | Batch: 200 | Loss: 1.043 | Acc: 63.02%
-2025-03-14 19:46:39,112 - train - INFO - Epoch: 41 | Batch: 300 | Loss: 1.041 | Acc: 63.14%
-2025-03-14 19:46:43,225 - train - INFO - Epoch: 41 | Test Loss: 1.087 | Test Acc: 61.82%
-2025-03-14 19:46:43,446 - train - INFO - Epoch: 42 | Batch: 0 | Loss: 0.974 | Acc: 67.97%
-2025-03-14 19:46:46,463 - train - INFO - Epoch: 42 | Batch: 100 | Loss: 1.031 | Acc: 63.10%
-2025-03-14 19:46:49,321 - train - INFO - Epoch: 42 | Batch: 200 | Loss: 1.046 | Acc: 62.82%
-2025-03-14 19:46:52,065 - train - INFO - Epoch: 42 | Batch: 300 | Loss: 1.040 | Acc: 62.91%
-2025-03-14 19:46:56,121 - train - INFO - Epoch: 42 | Test Loss: 1.088 | Test Acc: 61.92%
-2025-03-14 19:47:06,480 - train - INFO - Epoch: 43 | Batch: 0 | Loss: 0.959 | Acc: 64.84%
-2025-03-14 19:47:09,338 - train - INFO - Epoch: 43 | Batch: 100 | Loss: 1.025 | Acc: 63.60%
-2025-03-14 19:47:12,303 - train - INFO - Epoch: 43 | Batch: 200 | Loss: 1.028 | Acc: 63.58%
-2025-03-14 19:47:15,185 - train - INFO - Epoch: 43 | Batch: 300 | Loss: 1.034 | Acc: 63.35%
-2025-03-14 19:47:19,050 - train - INFO - Epoch: 43 | Test Loss: 1.164 | Test Acc: 59.61%
-2025-03-14 19:47:19,264 - train - INFO - Epoch: 44 | Batch: 0 | Loss: 1.079 | Acc: 55.47%
-2025-03-14 19:47:22,105 - train - INFO - Epoch: 44 | Batch: 100 | Loss: 1.025 | Acc: 63.51%
-2025-03-14 19:47:24,823 - train - INFO - Epoch: 44 | Batch: 200 | Loss: 1.025 | Acc: 63.38%
-2025-03-14 19:47:27,612 - train - INFO - Epoch: 44 | Batch: 300 | Loss: 1.028 | Acc: 63.24%
-2025-03-14 19:47:31,900 - train - INFO - Epoch: 44 | Test Loss: 1.046 | Test Acc: 62.74%
-2025-03-14 19:47:42,877 - train - INFO - Epoch: 45 | Batch: 0 | Loss: 1.033 | Acc: 62.50%
-2025-03-14 19:47:45,774 - train - INFO - Epoch: 45 | Batch: 100 | Loss: 1.033 | Acc: 63.99%
-2025-03-14 19:47:48,457 - train - INFO - Epoch: 45 | Batch: 200 | Loss: 1.038 | Acc: 63.33%
-2025-03-14 19:47:51,350 - train - INFO - Epoch: 45 | Batch: 300 | Loss: 1.043 | Acc: 62.95%
-2025-03-14 19:47:55,429 - train - INFO - Epoch: 45 | Test Loss: 1.055 | Test Acc: 62.21%
-2025-03-14 19:47:55,644 - train - INFO - Epoch: 46 | Batch: 0 | Loss: 1.122 | Acc: 57.03%
-2025-03-14 19:47:58,618 - train - INFO - Epoch: 46 | Batch: 100 | Loss: 1.020 | Acc: 63.92%
-2025-03-14 19:48:01,531 - train - INFO - Epoch: 46 | Batch: 200 | Loss: 1.039 | Acc: 63.16%
-2025-03-14 19:48:04,384 - train - INFO - Epoch: 46 | Batch: 300 | Loss: 1.039 | Acc: 63.08%
-2025-03-14 19:48:08,515 - train - INFO - Epoch: 46 | Test Loss: 1.151 | Test Acc: 58.80%
-2025-03-14 19:48:18,563 - train - INFO - Epoch: 47 | Batch: 0 | Loss: 1.005 | Acc: 61.72%
-2025-03-14 19:48:21,390 - train - INFO - Epoch: 47 | Batch: 100 | Loss: 1.011 | Acc: 64.18%
-2025-03-14 19:48:24,102 - train - INFO - Epoch: 47 | Batch: 200 | Loss: 1.029 | Acc: 63.72%
-2025-03-14 19:48:26,782 - train - INFO - Epoch: 47 | Batch: 300 | Loss: 1.028 | Acc: 63.71%
-2025-03-14 19:48:30,851 - train - INFO - Epoch: 47 | Test Loss: 1.091 | Test Acc: 61.88%
-2025-03-14 19:48:31,086 - train - INFO - Epoch: 48 | Batch: 0 | Loss: 1.034 | Acc: 66.41%
-2025-03-14 19:48:33,899 - train - INFO - Epoch: 48 | Batch: 100 | Loss: 1.013 | Acc: 63.99%
-2025-03-14 19:48:36,777 - train - INFO - Epoch: 48 | Batch: 200 | Loss: 1.031 | Acc: 63.33%
-2025-03-14 19:48:39,701 - train - INFO - Epoch: 48 | Batch: 300 | Loss: 1.034 | Acc: 63.14%
-2025-03-14 19:48:43,738 - train - INFO - Epoch: 48 | Test Loss: 1.051 | Test Acc: 63.01%
-2025-03-14 19:48:53,341 - train - INFO - Epoch: 49 | Batch: 0 | Loss: 0.940 | Acc: 69.53%
-2025-03-14 19:48:56,129 - train - INFO - Epoch: 49 | Batch: 100 | Loss: 1.022 | Acc: 63.68%
-2025-03-14 19:48:59,194 - train - INFO - Epoch: 49 | Batch: 200 | Loss: 1.027 | Acc: 63.31%
-2025-03-14 19:49:02,012 - train - INFO - Epoch: 49 | Batch: 300 | Loss: 1.029 | Acc: 63.34%
-2025-03-14 19:49:06,039 - train - INFO - Epoch: 49 | Test Loss: 1.045 | Test Acc: 63.33%
-2025-03-14 19:49:06,237 - train - INFO - Epoch: 50 | Batch: 0 | Loss: 0.902 | Acc: 63.28%
-2025-03-14 19:49:09,007 - train - INFO - Epoch: 50 | Batch: 100 | Loss: 1.025 | Acc: 63.56%
-2025-03-14 19:49:11,762 - train - INFO - Epoch: 50 | Batch: 200 | Loss: 1.026 | Acc: 63.59%
-2025-03-14 19:49:14,634 - train - INFO - Epoch: 50 | Batch: 300 | Loss: 1.031 | Acc: 63.42%
-2025-03-14 19:49:19,058 - train - INFO - Epoch: 50 | Test Loss: 1.069 | Test Acc: 62.55%
-2025-03-14 19:49:30,812 - train - INFO - 训练完成!
diff --git a/Image/MobileNetv3/code/train.py b/Image/MobileNetv3/code/train.py
deleted file mode 100644
index b3014ef3f545306f00a1df7fdaec4725f5e1245c..0000000000000000000000000000000000000000
--- a/Image/MobileNetv3/code/train.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
-from utils.dataset_utils import get_cifar10_dataloaders
-from utils.train_utils import train_model, train_model_data_augmentation, train_model_backdoor
-from utils.parse_args import parse_args
-from model import MobileNetV3
-
-def main():
-    # 解析命令行参数
-    args = parse_args()
-    
-    # 创建模型
-    model = MobileNetV3()
-    
-    if args.train_type == '0':
-        # 获取数据加载器
-        trainloader, testloader = get_cifar10_dataloaders(batch_size=args.batch_size, local_dataset_path=args.dataset_path)
-        # 训练模型
-        train_model(
-            model=model,
-            trainloader=trainloader,
-            testloader=testloader,
-            epochs=args.epochs,
-            lr=args.lr,
-            device=f'cuda:{args.gpu}',
-            save_dir='../model',
-            model_name='mobilenetv3',
-            save_type='0',
-            layer_name='avgpool',
-            interval=2
-        )
-    elif args.train_type == '1':
-        train_model_data_augmentation(
-            model, 
-            epochs=args.epochs, 
-            lr=args.lr, 
-            device=f'cuda:{args.gpu}', 
-            save_dir='../model', 
-            model_name='mobilenetv3',
-            batch_size=args.batch_size, 
-            num_workers=args.num_workers,
-            local_dataset_path=args.dataset_path
-        )
-    elif args.train_type == '2':
-        train_model_backdoor(
-            model, 
-            poison_ratio=args.poison_ratio, 
-            target_label=args.target_label, 
-            epochs=args.epochs, 
-            lr=args.lr,
-            device=f'cuda:{args.gpu}', 
-            save_dir='../model', 
-            model_name='mobilenetv3',
-            batch_size=args.batch_size, 
-            num_workers=args.num_workers,
-            local_dataset_path=args.dataset_path,
-            layer_name='avgpool',
-            interval=2
-        )
-
-if __name__ == '__main__':
-    main()
diff --git a/Image/MobileNetv3/dataset/.gitkeep b/Image/MobileNetv3/dataset/.gitkeep
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
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deleted file mode 100644
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deleted file mode 100644
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