Comments (6)
ShengcaiLiao
commented on June 3, 2024
1
现在QAConv 2.0训练速度很快了。在MSMT全集上（4101类，12万图像）单卡训练15个epoch只需要3.88小时。在RandPerson上（8000类，13万图像）单卡训练4个epoch只需要1.84小时。在UnrealPerson上（6799类，125万图像）单卡训练4个epoch只需要1.67小时。
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ShengcaiLiao
commented on June 3, 2024
应该正常，速度慢主要跟类别数有关，因为class memory的遍历卷积比较费时。最近在研究大数据上如何加速，目前还没有达到加速的同时准确性相差不多。
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ShengcaiLiao
commented on June 3, 2024
现在训练速度快一些，训练整个MSMT（4101类，126441张图，15个epochs）在单张V100 GPU上只用了不到17个小时。
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huangpan2507
commented on June 3, 2024
现在QAConv 2.0训练速度很快了。在MSMT全集上（4101类，12万图像）单卡训练15个epoch只需要3.88小时。在RandPerson上（8000类，13万图像）单卡训练4个epoch只需要1.84小时。在UnrealPerson上（6799类，125万图像）单卡训练4个epoch只需要1.67小时。
是要跑60个epoch吧
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ShengcaiLiao
commented on June 3, 2024
2.0只要跑15个epoch，虚拟数据库4个epoch。2.1版增加了自动控制epoch数，大部分公开数据库训练需要4-20个epoch。
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huangpan2507
commented on June 3, 2024
2.0只要跑15个epoch，虚拟数据库4个epoch。2.1版增加了自动控制epoch数，大部分公开数据库训练需要4-20个epoch。
可以可以，之前跑其他人的需要训练6天，太久了
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Related Issues (20)
- Question about backbone HOT 4
- Question about grad clipping in Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification HOT 2
- some questions HOT 1
- self.model.eval() HOT 2
- 这个代码可以跑多个训练集混合训练的实验吗？ HOT 1
- Dataset setting and memory overflow HOT 15
- Out of memory，--test_fea_batch --test_gal_batch --test_prob_batch all had seted to 128 HOT 16
- Issues about evaluators.py HOT 2
- Unable to use ClassMemoryLoss to train the model HOT 4
- Error about main.py HOT 1
- The Graph Sampling work 相关问题 HOT 2
- Graph Sampler HOT 3
- 复现的小问题 HOT 1
- graph sampling的疑问 HOT 4
- 关于s=1 HOT 2
- where is the "main_gs.py" HOT 1
- Can't find qaconv_loss HOT 5
- OSNet-IBN-GS代码 HOT 3
- 多源域训练/目标域测试 HOT 1
- 训练结果为0 HOT 8
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