jiawei151/sgiel_vireid

Official PyTorch Implementation of "Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification" (CVPR'23)

sgiel_vireid's Issues

关于baseline中使用EMA的问题

您好！感谢您精彩的工作。我看到本文的baseline，Rank-1精度大约到了70左右。我去掉您的所有项，训练出来精度为67.99。但是，每个step更新参数后，也执行了EMA更新，但是ema模型最终精度为1%，而且整个训练过程中测试也没啥变化，不知道什么情况。还有就是不加ema 67.99的精度是正常的吗？

关于HITSZ-VCM的问题

论文中有展示HITSZ-VCM上的结果，可是代码不支持在HITSZ-VCM上训练和测试，能提供更多关于HITSZ-VCM的实验细节吗？例如，训练和测试时是video-based还是image-based，video-based的序列长度等信息。

关于在SYSU-MM01上的性能问题

您好，根据您提供的代码和shape的数据集，我在SYSU-MM01数据集上进行了复现。但是我的复现并没有得到论文中的效果。最终跑了200个epoch，最好的效果出现在68个epoch。我的复现结果如下：
==> Preparing Data Loader...
68
current lr 0.001
Epoch:[68][0/695]L:0.0341(0.0341) L2:0.1142(0.1142) sL:0.0230(0.0230) w1:0.5949(0.5949) or:0.0023(0.0023) ML2:3.5368(3.5368) KL:0.8719(0.8719)
Epoch:[68][50/695]L:0.0217(0.0410) L2:0.0868(0.1193) sL:0.0165(0.0280) w1:0.6459(0.5807) or:0.0022(0.0022) ML2:2.8971(3.5661) KL:0.7459(0.8943)
Epoch:[68][100/695]L:0.0179(0.0399) L2:0.0711(0.1145) sL:0.0311(0.0286) w1:0.6091(0.5809) or:0.0023(0.0022) ML2:2.3823(3.4528) KL:0.7855(0.8820)
Epoch:[68][150/695]L:0.0415(0.0398) L2:0.1330(0.1147) sL:0.0267(0.0281) w1:0.6104(0.5807) or:0.0021(0.0022) ML2:3.8870(3.4472) KL:0.8610(0.8742)
Epoch:[68][200/695]L:0.0621(0.0401) L2:0.1400(0.1156) sL:0.0349(0.0285) w1:0.4506(0.5763) or:0.0023(0.0022) ML2:4.5594(3.4788) KL:0.9806(0.8781)
Epoch:[68][250/695]L:0.0520(0.0389) L2:0.1278(0.1129) sL:0.0287(0.0281) w1:0.6022(0.5826) or:0.0024(0.0022) ML2:3.1366(3.4127) KL:0.9168(0.8736)
Epoch:[68][300/695]L:0.0466(0.0392) L2:0.1424(0.1133) sL:0.0282(0.0280) w1:0.5992(0.5830) or:0.0023(0.0022) ML2:4.0995(3.4141) KL:0.9180(0.8747)
Epoch:[68][350/695]L:0.0210(0.0388) L2:0.0708(0.1128) sL:0.0247(0.0279) w1:0.7259(0.5835) or:0.0023(0.0022) ML2:2.3554(3.4080) KL:0.7897(0.8747)
Epoch:[68][400/695]L:0.0293(0.0386) L2:0.0920(0.1121) sL:0.0273(0.0279) w1:0.6834(0.5844) or:0.0022(0.0022) ML2:3.0099(3.3912) KL:0.8538(0.8732)
Epoch:[68][450/695]L:0.0238(0.0390) L2:0.0866(0.1127) sL:0.0356(0.0282) w1:0.7104(0.5832) or:0.0023(0.0022) ML2:2.6720(3.4074) KL:0.8792(0.8757)
Epoch:[68][500/695]L:0.0432(0.0390) L2:0.1183(0.1127) sL:0.0270(0.0281) w1:0.6754(0.5852) or:0.0022(0.0022) ML2:2.9553(3.4083) KL:0.8770(0.8772)
Epoch:[68][550/695]L:0.0225(0.0386) L2:0.0828(0.1121) sL:0.0203(0.0279) w1:0.4978(0.5850) or:0.0023(0.0022) ML2:2.6331(3.3943) KL:0.7263(0.8749)
Epoch:[68][600/695]L:0.0577(0.0386) L2:0.1230(0.1120) sL:0.0318(0.0278) w1:0.4661(0.5841) or:0.0022(0.0022) ML2:3.7425(3.3959) KL:0.9794(0.8749)
Epoch:[68][650/695]L:0.0268(0.0387) L2:0.1098(0.1123) sL:0.0305(0.0278) w1:0.6184(0.5833) or:0.0023(0.0022) ML2:3.7047(3.4044) KL:0.9451(0.8748)
Test Epoch: 68
Extracting Gallery Feature...
Extracting Time: 1.526
Extracting Query Feature...
Extracting Time: 6.547
Evaluation Time: 5.361
Extracting Gallery Feature...
Extracting Time: 1.545
Extracting Query Feature...
Extracting Time: 6.483
Evaluation Time: 5.422
POOL: Rank-1: 67.84% | Rank-5: 88.98% | Rank-10: 94.74%| Rank-20: 98.66%| mAP: 65.55%| mINP: 53.43%
FC: Rank-1: 70.42% | Rank-5: 91.38% | Rank-10: 95.98%| Rank-20: 98.74%| mAP: 67.05%| mINP: 53.69%
Best Epoch [33]
------------------ema eval------------------
POOL: Rank-1: 68.45% | Rank-5: 89.53% | Rank-10: 94.61%| Rank-20: 98.24%| mAP: 66.25%| mINP: 54.22%
FC: Rank-1: 72.31% | Rank-5: 91.48% | Rank-10: 95.69%| Rank-20: 98.40%| mAP: 68.09%| mINP: 54.34%
Best Epoch [68]
我的超参数都是根据代码中的默认设置的。请问这可能是什么问题呢

How do you manage with failed instances??

it seems that you trained your proposed model with the shape data prepared (not generating them when training)
but when i try SCHP model on SYSU, there is a phenomenon that SCHP significantly fails on images with under certain resolution. it outputs totally nothing. how did you solve that? did you manually labeled them or use another model?

and additionally, how did you manage images that have more than 1 person? if more than 1 person exist in an image, SCHP detect all human parts.

shape 图片的通道

您好，请问下您生成的SHAPE 图片是单通道的该怎么处理呢， 直接广播成三个通道吗？

SYSU_MM01_SHAPE

I want to ask how to obtain SYSU_MM01_SHAPE and SYSU_MM01_MASK ?

how to test it？

body shape files

Could you share your .npy files for the body shape of SYSU-MM01 and RegDB datasets?

data_path路径咨询

我想问一下，data_path1里面是您分享的shape数据集，data_path2文件夹下放的是什么呢？