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PVT Large deosn't converge #6

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VictorLlu opened this issue Mar 3, 2021 · 3 comments
Closed

PVT Large deosn't converge #6

VictorLlu opened this issue Mar 3, 2021 · 3 comments

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@VictorLlu
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VictorLlu commented Mar 3, 2021 •
edited

Thanks for your great work. But when I trained PVT Large (pvt_large) as your default settings, the model didn't converge. The loss declined correctly in the first 37 epochs and the accuracy went to 57% but the model went wrong at 38th epoch. I used your code without any change. What's the problem? Thank you!

Below is a part of my training log.

Test: Total time: 0:01:55 (0.4429 s / it)

  • Acc@1 57.009 Acc@5 81.174 loss 1.948
    Accuracy of the network on the 50000 test images: 57.0%
    Max accuracy: 57.01%
    Epoch: [38] [ 0/1251] eta: 2:06:33 lr: 0.000963 loss: 4.9324 (4.9324) time: 6.0701 data: 3.6057 max mem: 25529
    Epoch: [38] [ 10/1251] eta: 0:31:59 lr: 0.000963 loss: 4.5930 (4.5768) time: 1.5465 data: 0.3281 max mem: 25529
    Epoch: [38] [ 20/1251] eta: 0:27:07 lr: 0.000963 loss: 4.6624 (4.6160) time: 1.0843 data: 0.0003 max mem: 25529
    Epoch: [38] [ 30/1251] eta: 0:25:15 lr: 0.000963 loss: 4.7355 (4.5806) time: 1.0737 data: 0.0003 max mem: 25529
    Epoch: [38] [ 40/1251] eta: 0:24:16 lr: 0.000963 loss: 4.6986 (4.5811) time: 1.0784 data: 0.0003 max mem: 25529
    Epoch: [38] [ 50/1251] eta: 0:23:33 lr: 0.000963 loss: 4.6986 (4.5609) time: 1.0766 data: 0.0003 max mem: 25529
    Epoch: [38] [ 60/1251] eta: 0:23:07 lr: 0.000963 loss: 4.7104 (4.5901) time: 1.0864 data: 0.0003 max mem: 25529
    Epoch: [38] [ 70/1251] eta: 0:22:39 lr: 0.000963 loss: 4.8095 (4.6143) time: 1.0854 data: 0.0003 max mem: 25529
    Epoch: [38] [ 80/1251] eta: 0:22:17 lr: 0.000963 loss: 4.7373 (4.5898) time: 1.0721 data: 0.0003 max mem: 25529
    Epoch: [38] [ 90/1251] eta: 0:21:55 lr: 0.000963 loss: 4.4603 (4.5742) time: 1.0696 data: 0.0003 max mem: 25529
    Epoch: [38] [ 100/1251] eta: 0:21:37 lr: 0.000963 loss: 4.5539 (4.5777) time: 1.0682 data: 0.0003 max mem: 25529
    Epoch: [38] [ 110/1251] eta: 0:21:21 lr: 0.000963 loss: 4.9701 (4.5993) time: 1.0787 data: 0.0003 max mem: 25529
    Epoch: [38] [ 120/1251] eta: 0:21:06 lr: 0.000963 loss: 4.9029 (4.5914) time: 1.0811 data: 0.0003 max mem: 25529
    Epoch: [38] [ 130/1251] eta: 0:20:50 lr: 0.000963 loss: 4.7300 (4.5999) time: 1.0711 data: 0.0003 max mem: 25529
    Epoch: [38] [ 140/1251] eta: 0:20:35 lr: 0.000963 loss: 4.7998 (4.5936) time: 1.0630 data: 0.0003 max mem: 25529
    Epoch: [38] [ 150/1251] eta: 0:20:23 lr: 0.000963 loss: 4.8562 (4.5969) time: 1.0850 data: 0.0003 max mem: 25529
    Epoch: [38] [ 160/1251] eta: 0:20:09 lr: 0.000963 loss: 4.8583 (4.5961) time: 1.0852 data: 0.0003 max mem: 25529
    Epoch: [38] [ 170/1251] eta: 0:19:55 lr: 0.000963 loss: 4.8583 (4.6029) time: 1.0677 data: 0.0003 max mem: 25529
    Epoch: [38] [ 180/1251] eta: 0:19:42 lr: 0.000963 loss: 5.0298 (4.6202) time: 1.0675 data: 0.0003 max mem: 25529
    Epoch: [38] [ 190/1251] eta: 0:19:28 lr: 0.000963 loss: 4.8480 (4.6175) time: 1.0634 data: 0.0003 max mem: 25529
    Epoch: [38] [ 200/1251] eta: 0:19:15 lr: 0.000963 loss: 4.6446 (4.6124) time: 1.0629 data: 0.0003 max mem: 25529
    Epoch: [38] [ 210/1251] eta: 0:19:04 lr: 0.000963 loss: 4.8329 (4.6245) time: 1.0741 data: 0.0003 max mem: 25529
    Epoch: [38] [ 220/1251] eta: 0:18:52 lr: 0.000963 loss: 4.9058 (4.6362) time: 1.0833 data: 0.0003 max mem: 25529
    Epoch: [38] [ 230/1251] eta: 0:18:40 lr: 0.000963 loss: 4.7250 (4.6332) time: 1.0764 data: 0.0003 max mem: 25529
    Epoch: [38] [ 240/1251] eta: 0:18:28 lr: 0.000963 loss: 4.6894 (4.6391) time: 1.0808 data: 0.0003 max mem: 25529
    Epoch: [38] [ 250/1251] eta: 0:18:16 lr: 0.000963 loss: 4.8600 (4.6438) time: 1.0789 data: 0.0003 max mem: 25529
    Epoch: [38] [ 260/1251] eta: 0:18:04 lr: 0.000963 loss: 4.9939 (4.6550) time: 1.0710 data: 0.0003 max mem: 25529
    Epoch: [38] [ 270/1251] eta: 0:17:53 lr: 0.000963 loss: 4.7281 (4.6478) time: 1.0717 data: 0.0003 max mem: 25529
    Epoch: [38] [ 280/1251] eta: 0:17:41 lr: 0.000963 loss: 4.3858 (4.6383) time: 1.0664 data: 0.0003 max mem: 25529
    Epoch: [38] [ 290/1251] eta: 0:17:29 lr: 0.000963 loss: 4.5126 (4.6390) time: 1.0627 data: 0.0003 max mem: 25529
    Epoch: [38] [ 300/1251] eta: 0:17:17 lr: 0.000963 loss: 4.3964 (4.6302) time: 1.0638 data: 0.0003 max mem: 25529
    Epoch: [38] [ 310/1251] eta: 0:17:05 lr: 0.000963 loss: 4.3964 (4.6284) time: 1.0683 data: 0.0003 max mem: 25529
    Epoch: [38] [ 320/1251] eta: 0:16:54 lr: 0.000963 loss: 4.4917 (4.6220) time: 1.0689 data: 0.0003 max mem: 25529
    Epoch: [38] [ 330/1251] eta: 0:16:42 lr: 0.000963 loss: 4.7606 (4.6335) time: 1.0695 data: 0.0003 max mem: 25529
    Epoch: [38] [ 340/1251] eta: 0:16:31 lr: 0.000963 loss: 5.0333 (4.6346) time: 1.0699 data: 0.0003 max mem: 25529
    Epoch: [38] [ 350/1251] eta: 0:16:20 lr: 0.000963 loss: 4.6795 (4.6276) time: 1.0700 data: 0.0003 max mem: 25529
    Epoch: [38] [ 360/1251] eta: 0:16:08 lr: 0.000963 loss: 4.7723 (4.6305) time: 1.0728 data: 0.0003 max mem: 25529
    Epoch: [38] [ 370/1251] eta: 0:15:57 lr: 0.000963 loss: 4.8322 (4.6305) time: 1.0767 data: 0.0003 max mem: 25529
    Epoch: [38] [ 380/1251] eta: 0:15:46 lr: 0.000963 loss: 4.7535 (4.6310) time: 1.0725 data: 0.0003 max mem: 25529
    Epoch: [38] [ 390/1251] eta: 0:15:35 lr: 0.000963 loss: 4.5236 (4.6247) time: 1.0746 data: 0.0003 max mem: 25529
    Epoch: [38] [ 400/1251] eta: 0:15:24 lr: 0.000963 loss: 4.5129 (4.6280) time: 1.0783 data: 0.0003 max mem: 25529
    Epoch: [38] [ 410/1251] eta: 0:15:13 lr: 0.000963 loss: 4.6520 (4.6250) time: 1.0803 data: 0.0003 max mem: 25529
    Epoch: [38] [ 420/1251] eta: 0:15:02 lr: 0.000963 loss: 4.6115 (4.6235) time: 1.0841 data: 0.0003 max mem: 25529
    Epoch: [38] [ 430/1251] eta: 0:14:51 lr: 0.000963 loss: 4.5550 (4.6176) time: 1.0788 data: 0.0003 max mem: 25529
    Epoch: [38] [ 440/1251] eta: 0:14:40 lr: 0.000963 loss: 4.3985 (4.6097) time: 1.0745 data: 0.0003 max mem: 25529
    Epoch: [38] [ 450/1251] eta: 0:14:29 lr: 0.000963 loss: 4.5041 (4.6144) time: 1.0711 data: 0.0004 max mem: 25529
    Epoch: [38] [ 460/1251] eta: 0:14:18 lr: 0.000963 loss: 4.7949 (4.6127) time: 1.0769 data: 0.0003 max mem: 25529
    Epoch: [38] [ 470/1251] eta: 0:14:07 lr: 0.000963 loss: 4.7556 (4.6148) time: 1.0773 data: 0.0003 max mem: 25529
    Epoch: [38] [ 480/1251] eta: 0:13:56 lr: 0.000963 loss: 5.0523 (4.6200) time: 1.0845 data: 0.0003 max mem: 25529
    Epoch: [38] [ 490/1251] eta: 0:13:45 lr: 0.000963 loss: 4.5865 (4.6152) time: 1.0781 data: 0.0003 max mem: 25529
    Epoch: [38] [ 500/1251] eta: 0:13:34 lr: 0.000963 loss: 4.6311 (4.6210) time: 1.0776 data: 0.0003 max mem: 25529
    Epoch: [38] [ 510/1251] eta: 0:13:23 lr: 0.000963 loss: 4.8767 (4.6208) time: 1.0855 data: 0.0003 max mem: 25529
    Epoch: [38] [ 520/1251] eta: 0:13:13 lr: 0.000963 loss: 4.7439 (4.6204) time: 1.0891 data: 0.0003 max mem: 25529
    Epoch: [38] [ 530/1251] eta: 0:13:02 lr: 0.000963 loss: 4.7974 (4.6190) time: 1.0813 data: 0.0003 max mem: 25529
    Epoch: [38] [ 540/1251] eta: 0:12:51 lr: 0.000963 loss: 4.6865 (4.6171) time: 1.0676 data: 0.0003 max mem: 25529
    Epoch: [38] [ 550/1251] eta: 0:12:40 lr: 0.000963 loss: 4.4560 (4.6144) time: 1.0727 data: 0.0003 max mem: 25529
    Epoch: [38] [ 560/1251] eta: 0:12:29 lr: 0.000963 loss: 4.2302 (4.6069) time: 1.0761 data: 0.0003 max mem: 25529
    Epoch: [38] [ 570/1251] eta: 0:12:18 lr: 0.000963 loss: 4.3246 (4.6080) time: 1.0741 data: 0.0003 max mem: 25529
    Epoch: [38] [ 580/1251] eta: 0:12:07 lr: 0.000963 loss: 4.5513 (4.6052) time: 1.0661 data: 0.0003 max mem: 25529
    Epoch: [38] [ 590/1251] eta: 0:11:56 lr: 0.000963 loss: 4.4924 (4.6075) time: 1.0740 data: 0.0003 max mem: 25529
    Epoch: [38] [ 600/1251] eta: 0:11:45 lr: 0.000963 loss: 4.5949 (4.6052) time: 1.0817 data: 0.0003 max mem: 25529
    Epoch: [38] [ 610/1251] eta: 0:11:34 lr: 0.000963 loss: 4.5321 (4.6035) time: 1.0638 data: 0.0003 max mem: 25529
    Epoch: [38] [ 620/1251] eta: 0:11:23 lr: 0.000963 loss: 4.7689 (4.6075) time: 1.0604 data: 0.0003 max mem: 25529
    Epoch: [38] [ 630/1251] eta: 0:11:12 lr: 0.000963 loss: 4.7689 (4.6088) time: 1.0649 data: 0.0003 max mem: 25529
    Epoch: [38] [ 640/1251] eta: 0:11:01 lr: 0.000963 loss: 4.4721 (4.6039) time: 1.0580 data: 0.0003 max mem: 25529
    Epoch: [38] [ 650/1251] eta: 0:10:50 lr: 0.000963 loss: 4.5410 (4.6067) time: 1.0654 data: 0.0003 max mem: 25529
    Epoch: [38] [ 660/1251] eta: 0:10:39 lr: 0.000963 loss: 4.5659 (4.5996) time: 1.0689 data: 0.0003 max mem: 25529
    Epoch: [38] [ 670/1251] eta: 0:10:28 lr: 0.000963 loss: 4.4456 (4.5999) time: 1.0727 data: 0.0003 max mem: 25529
    Epoch: [38] [ 680/1251] eta: 0:10:17 lr: 0.000963 loss: 4.8766 (4.6035) time: 1.0818 data: 0.0003 max mem: 25529
    Epoch: [38] [ 690/1251] eta: 0:10:06 lr: 0.000963 loss: 4.8766 (4.6041) time: 1.0854 data: 0.0003 max mem: 25529
    Epoch: [38] [ 700/1251] eta: 0:09:55 lr: 0.000963 loss: 4.9327 (4.6104) time: 1.0805 data: 0.0003 max mem: 25529
    Epoch: [38] [ 710/1251] eta: 0:09:44 lr: 0.000963 loss: 5.0049 (4.6129) time: 1.0702 data: 0.0003 max mem: 25529
    Epoch: [38] [ 720/1251] eta: 0:09:34 lr: 0.000963 loss: 4.6922 (4.6117) time: 1.0673 data: 0.0003 max mem: 25529
    Epoch: [38] [ 730/1251] eta: 0:09:23 lr: 0.000963 loss: 4.6331 (4.6107) time: 1.0810 data: 0.0003 max mem: 25529
    Epoch: [38] [ 740/1251] eta: 0:09:12 lr: 0.000963 loss: 4.5547 (4.6111) time: 1.0795 data: 0.0003 max mem: 25529
    Epoch: [38] [ 750/1251] eta: 0:09:01 lr: 0.000963 loss: 4.8843 (4.6181) time: 1.0719 data: 0.0003 max mem: 25529
    Epoch: [38] [ 760/1251] eta: 0:08:50 lr: 0.000963 loss: 4.8843 (4.6160) time: 1.0851 data: 0.0003 max mem: 25529
    Epoch: [38] [ 770/1251] eta: 0:08:40 lr: 0.000963 loss: 4.2934 (4.6119) time: 1.0840 data: 0.0003 max mem: 25529
    Epoch: [38] [ 780/1251] eta: 0:08:29 lr: 0.000963 loss: 4.1930 (4.6087) time: 1.0784 data: 0.0003 max mem: 25529
    Epoch: [38] [ 790/1251] eta: 0:08:18 lr: 0.000963 loss: 4.4176 (4.6073) time: 1.0748 data: 0.0003 max mem: 25529
    Epoch: [38] [ 800/1251] eta: 0:08:07 lr: 0.000963 loss: 4.7402 (4.6115) time: 1.0681 data: 0.0003 max mem: 25529
    Epoch: [38] [ 810/1251] eta: 0:07:56 lr: 0.000963 loss: 4.7749 (4.6094) time: 1.0713 data: 0.0003 max mem: 25529
    Epoch: [38] [ 820/1251] eta: 0:07:45 lr: 0.000963 loss: 4.6709 (4.6079) time: 1.0732 data: 0.0003 max mem: 25529
    Epoch: [38] [ 830/1251] eta: 0:07:34 lr: 0.000963 loss: 4.7506 (4.6088) time: 1.0641 data: 0.0003 max mem: 25529
    Epoch: [38] [ 840/1251] eta: 0:07:23 lr: 0.000963 loss: 4.8636 (4.6112) time: 1.0592 data: 0.0003 max mem: 25529
    Epoch: [38] [ 850/1251] eta: 0:07:13 lr: 0.000963 loss: 4.9930 (4.6116) time: 1.0767 data: 0.0003 max mem: 25529
    Epoch: [38] [ 860/1251] eta: 0:07:02 lr: 0.000963 loss: 5.0639 (4.6155) time: 1.0766 data: 0.0003 max mem: 25529
    Epoch: [38] [ 870/1251] eta: 0:06:51 lr: 0.000963 loss: 5.0486 (4.6160) time: 1.0683 data: 0.0003 max mem: 25529
    Epoch: [38] [ 880/1251] eta: 0:06:40 lr: 0.000963 loss: 4.6785 (4.6145) time: 1.0654 data: 0.0003 max mem: 25529
    Epoch: [38] [ 890/1251] eta: 0:06:29 lr: 0.000963 loss: 4.6382 (4.6126) time: 1.0603 data: 0.0003 max mem: 25529
    Epoch: [38] [ 900/1251] eta: 0:06:18 lr: 0.000963 loss: 4.9989 (4.6179) time: 1.0642 data: 0.0003 max mem: 25529
    Epoch: [38] [ 910/1251] eta: 0:06:08 lr: 0.000963 loss: 5.0227 (4.6205) time: 1.0740 data: 0.0003 max mem: 25529
    Epoch: [38] [ 920/1251] eta: 0:05:57 lr: 0.000963 loss: 4.7505 (4.6198) time: 1.0733 data: 0.0003 max mem: 25529
    Epoch: [38] [ 930/1251] eta: 0:05:46 lr: 0.000963 loss: 4.6593 (4.6196) time: 1.0636 data: 0.0003 max mem: 25529
    Epoch: [38] [ 940/1251] eta: 0:05:35 lr: 0.000963 loss: 4.7349 (4.6184) time: 1.0697 data: 0.0003 max mem: 25529
    Epoch: [38] [ 950/1251] eta: 0:05:24 lr: 0.000963 loss: 4.8424 (4.6185) time: 1.0741 data: 0.0003 max mem: 25529
    Epoch: [38] [ 960/1251] eta: 0:05:13 lr: 0.000963 loss: 4.5308 (4.6170) time: 1.0704 data: 0.0003 max mem: 25529
    Epoch: [38] [ 970/1251] eta: 0:05:03 lr: 0.000963 loss: 4.6764 (4.6186) time: 1.0749 data: 0.0003 max mem: 25529
    Epoch: [38] [ 980/1251] eta: 0:04:52 lr: 0.000963 loss: 4.6764 (4.6176) time: 1.0768 data: 0.0004 max mem: 25529
    Epoch: [38] [ 990/1251] eta: 0:04:41 lr: 0.000963 loss: 4.5145 (4.6176) time: 1.0677 data: 0.0004 max mem: 25529
    Epoch: [38] [1000/1251] eta: 0:04:30 lr: 0.000963 loss: 4.5645 (4.6202) time: 1.0686 data: 0.0003 max mem: 25529
    Epoch: [38] [1010/1251] eta: 0:04:19 lr: 0.000963 loss: 5.3548 (4.6373) time: 1.0613 data: 0.0003 max mem: 25529
    Epoch: [38] [1020/1251] eta: 0:04:09 lr: 0.000963 loss: 6.9353 (4.6599) time: 1.0595 data: 0.0003 max mem: 25529
    Epoch: [38] [1030/1251] eta: 0:03:58 lr: 0.000963 loss: 6.9423 (4.6820) time: 1.0729 data: 0.0003 max mem: 25529
    Epoch: [38] [1040/1251] eta: 0:03:47 lr: 0.000963 loss: 6.9381 (4.7036) time: 1.0715 data: 0.0003 max mem: 25529
    Epoch: [38] [1050/1251] eta: 0:03:36 lr: 0.000963 loss: 6.9351 (4.7248) time: 1.0717 data: 0.0003 max mem: 25529
    Epoch: [38] [1060/1251] eta: 0:03:25 lr: 0.000963 loss: 6.9315 (4.7456) time: 1.0655 data: 0.0003 max mem: 25529
    Epoch: [38] [1070/1251] eta: 0:03:15 lr: 0.000963 loss: 6.9319 (4.7660) time: 1.0609 data: 0.0003 max mem: 25529
    Epoch: [38] [1080/1251] eta: 0:03:04 lr: 0.000963 loss: 6.9287 (4.7860) time: 1.0717 data: 0.0003 max mem: 25529
    Epoch: [38] [1090/1251] eta: 0:02:53 lr: 0.000963 loss: 6.9198 (4.8055) time: 1.0834 data: 0.0003 max mem: 25529
    Epoch: [38] [1100/1251] eta: 0:02:42 lr: 0.000963 loss: 6.9219 (4.8248) time: 1.0835 data: 0.0003 max mem: 25529
    Epoch: [38] [1110/1251] eta: 0:02:32 lr: 0.000963 loss: 6.9286 (4.8437) time: 1.1036 data: 0.0003 max mem: 25529
    Epoch: [38] [1120/1251] eta: 0:02:21 lr: 0.000963 loss: 6.9209 (4.8622) time: 1.0965 data: 0.0003 max mem: 25529
    Epoch: [38] [1130/1251] eta: 0:02:10 lr: 0.000963 loss: 6.9212 (4.8804) time: 1.0701 data: 0.0003 max mem: 25529
    Epoch: [38] [1140/1251] eta: 0:01:59 lr: 0.000963 loss: 6.9192 (4.8983) time: 1.0686 data: 0.0003 max mem: 25529
    Epoch: [38] [1150/1251] eta: 0:01:48 lr: 0.000963 loss: 6.9192 (4.9159) time: 1.0640 data: 0.0003 max mem: 25529
    Epoch: [38] [1160/1251] eta: 0:01:38 lr: 0.000963 loss: 6.9231 (4.9332) time: 1.0687 data: 0.0003 max mem: 25529
    Epoch: [38] [1170/1251] eta: 0:01:27 lr: 0.000963 loss: 6.9241 (4.9502) time: 1.0702 data: 0.0003 max mem: 25529
    Epoch: [38] [1180/1251] eta: 0:01:16 lr: 0.000963 loss: 6.9240 (4.9669) time: 1.0687 data: 0.0003 max mem: 25529
    Epoch: [38] [1190/1251] eta: 0:01:05 lr: 0.000963 loss: 6.9198 (4.9833) time: 1.0668 data: 0.0003 max mem: 25529
    Epoch: [38] [1200/1251] eta: 0:00:54 lr: 0.000963 loss: 6.9150 (4.9993) time: 1.0864 data: 0.0003 max mem: 25529
    Epoch: [38] [1210/1251] eta: 0:00:44 lr: 0.000963 loss: 6.9144 (5.0152) time: 1.0855 data: 0.0003 max mem: 25529
    Epoch: [38] [1220/1251] eta: 0:00:33 lr: 0.000963 loss: 6.9167 (5.0308) time: 1.0714 data: 0.0003 max mem: 25529
    Epoch: [38] [1230/1251] eta: 0:00:22 lr: 0.000963 loss: 6.9167 (5.0461) time: 1.0702 data: 0.0003 max mem: 25529
    Epoch: [38] [1240/1251] eta: 0:00:11 lr: 0.000963 loss: 6.9135 (5.0612) time: 1.0574 data: 0.0005 max mem: 25529
    Epoch: [38] [1250/1251] eta: 0:00:01 lr: 0.000963 loss: 6.9179 (5.0760) time: 1.0532 data: 0.0004 max mem: 25529
    Epoch: [38] Total time: 0:22:28 (1.0781 s / it)
    Averaged stats: lr: 0.000963 loss: 6.9179 (5.0558)
    Test: [ 0/261] eta: 0:31:19 loss: 6.8103 (6.8103) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.0000) time: 7.2018 data: 6.7932 max mem: 25529
    Test: [ 10/261] eta: 0:04:17 loss: 6.9766 (6.9290) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.0000) time: 1.0263 data: 0.6262 max mem: 25529
    Test: [ 20/261] eta: 0:02:56 loss: 6.9750 (6.9375) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.0000) time: 0.4103 data: 0.0066 max mem: 25529
    Test: [ 30/261] eta: 0:02:25 loss: 6.9495 (6.9457) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.0000) time: 0.4091 data: 0.0024 max mem: 25529
    Test: [ 40/261] eta: 0:02:06 loss: 6.9158 (6.9258) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.6352) time: 0.4017 data: 0.0010 max mem: 25529
    Test: [ 50/261] eta: 0:01:53 loss: 6.8871 (6.9364) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.5106) time: 0.3975 data: 0.0007 max mem: 25529
    Test: [ 60/261] eta: 0:01:43 loss: 6.9326 (6.9323) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.4269) time: 0.3969 data: 0.0007 max mem: 25529
    Test: [ 70/261] eta: 0:01:35 loss: 6.8942 (6.9268) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.3668) time: 0.3951 data: 0.0016 max mem: 25529
    Test: [ 80/261] eta: 0:01:27 loss: 6.8974 (6.9259) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.3215) time: 0.3954 data: 0.0025 max mem: 25529
    Test: [ 90/261] eta: 0:01:21 loss: 6.9066 (6.9268) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.2862) time: 0.3983 data: 0.0017 max mem: 25529
    Test: [100/261] eta: 0:01:15 loss: 6.9556 (6.9323) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.2578) time: 0.3960 data: 0.0009 max mem: 25529
    Test: [110/261] eta: 0:01:09 loss: 6.9268 (6.9298) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.2346) time: 0.3962 data: 0.0010 max mem: 25529
    Test: [120/261] eta: 0:01:04 loss: 6.8970 (6.9270) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.2152) time: 0.4211 data: 0.0242 max mem: 25529
    Test: [130/261] eta: 0:00:59 loss: 6.8970 (6.9251) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.1988) time: 0.4183 data: 0.0242 max mem: 25529
    Test: [140/261] eta: 0:00:54 loss: 6.9251 (6.9268) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.3694) time: 0.3986 data: 0.0018 max mem: 25529
    Test: [150/261] eta: 0:00:49 loss: 6.9534 (6.9264) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.3449) time: 0.4021 data: 0.0045 max mem: 25529
    Test: [160/261] eta: 0:00:45 loss: 6.8927 (6.9243) acc1: 0.0000 (0.1617) acc5: 0.0000 (0.4852) time: 0.4124 data: 0.0182 max mem: 25529
    Test: [170/261] eta: 0:00:40 loss: 6.8886 (6.9231) acc1: 0.0000 (0.1523) acc5: 0.0000 (0.4569) time: 0.4112 data: 0.0157 max mem: 25529
    Test: [180/261] eta: 0:00:35 loss: 6.9188 (6.9233) acc1: 0.0000 (0.1439) acc5: 0.0000 (0.4316) time: 0.3997 data: 0.0016 max mem: 25529
    Test: [190/261] eta: 0:00:31 loss: 6.9170 (6.9216) acc1: 0.0000 (0.1363) acc5: 0.0000 (0.4090) time: 0.4233 data: 0.0265 max mem: 25529
    Test: [200/261] eta: 0:00:26 loss: 6.9137 (6.9224) acc1: 0.0000 (0.1296) acc5: 0.0000 (0.3887) time: 0.4463 data: 0.0536 max mem: 25529
    Test: [210/261] eta: 0:00:22 loss: 6.9097 (6.9210) acc1: 0.0000 (0.1234) acc5: 0.0000 (0.3703) time: 0.5000 data: 0.1046 max mem: 25529
    Test: [220/261] eta: 0:00:18 loss: 6.8762 (6.9184) acc1: 0.0000 (0.1178) acc5: 0.0000 (0.3535) time: 0.4731 data: 0.0773 max mem: 25529
    Test: [230/261] eta: 0:00:13 loss: 6.8775 (6.9185) acc1: 0.0000 (0.1127) acc5: 0.0000 (0.4509) time: 0.3974 data: 0.0048 max mem: 25529
    Test: [240/261] eta: 0:00:09 loss: 6.9246 (6.9183) acc1: 0.0000 (0.1081) acc5: 0.0000 (0.4322) time: 0.4009 data: 0.0050 max mem: 25529
    Test: [250/261] eta: 0:00:04 loss: 6.9132 (6.9190) acc1: 0.0000 (0.1038) acc5: 0.0000 (0.5188) time: 0.3949 data: 0.0010 max mem: 25529
    Test: [260/261] eta: 0:00:00 loss: 6.9128 (6.9180) acc1: 0.0000 (0.1000) acc5: 0.0000 (0.5000) time: 0.3788 data: 0.0001 max mem: 25529
    Test: Total time: 0:01:54 (0.4370 s / it)
  • Acc@1 0.100 Acc@5 0.500 loss 6.918
    Accuracy of the network on the 50000 test images: 0.1%
    Max accuracy: 57.01%
@whai362
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whai362 commented Mar 5, 2021

Thanks for your attention.
This problem may be caused by the drop path rate.
For PVT_large, we first set the drop path rate to 0, in the first 100 epochs.
image.

Could you check the ``main.py'' again?

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whai362 commented Mar 5, 2021

#2

@whai362 whai362 closed this as completed Mar 5, 2021
@whai362
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whai362 commented Mar 5, 2021

If it still doesn't work, please let me know.
You can email me at wangwenhai362@163.com.
I don't check the Github issue often.

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