Question about memory updating in q_hard about cluster-contrast-reid HOT 4 CLOSED

I have the same question, the performance decreases to 77.8% (mAP) and 89.9% (top-1) when choosing the "use-hard" option.

Could you tell me why CM_hard does not show any advantages compared to CM?

from cluster-contrast-reid.

Sorry that we didn't not provides the CM_hard command in readme.md. I will update it.
For this command:
CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/cluster_contrast_train_usl.py -b 256 -a resnet_ibn50a -d market1501 --iters 400 --momentum 0.1 --eps 0.4 --num-instances 16 --pooling-type gem --use-hard --logs-dir /data0/developer/cluster-contrast/examples/logs/gem-hard
You will get result:
Mean AP: 87.0%
CMC Scores:
top-1 94.6%
top-5 98.2%
top-10 98.8%
which is much higher than paper result since we use gem pooling.

If use average pooling like:
CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/cluster_contrast_train_usl.py -b 256 -a resnet_ibn50a -d market1501 --iters 400 --momentum 0.1 --eps 0.4 --num-instances 16 --pooling-type avg --use-hard
You will get result similar or a litter higher than our paper:
Mean AP: 84.5%
CMC Scores:
top-1 93.6%
top-5 97.5%
top-10 98.4%

Thank!

from cluster-contrast-reid.

Using CM_hard will get better result, but it requires carefully selected hyper-parameters. Generally, we recommend using CM first, and then try CM_hard for different hyper-parameters.

from cluster-contrast-reid.

from my test，I choose batchsize 128 model resnet dataset market1501 --iters 400 --momentum 0.1 --eps 0.4 --num-instances 16 --pooling-type gem . when I choose use-hard ,mAP is 78.0. but when I use common CM ,mAP is 80.8

from cluster-contrast-reid.