Comments (8)
For better performance ,I found another implementation:
Alignedreid++: Dynamically Matching Local Information for Person Re-Identification.
Code
from alignedreid-re-production-pytorch.
我的实验结果是这样的,如果有更好的实验结果,欢迎告知哈。
from alignedreid-re-production-pytorch.
@huanghoujing Hi houjing! But when I trained the model with local branch(local loss and local_dist_own_hard_sample=True) on three datasets, it showed no ~1 point improvement, even worse! I am not clear why? Could you please show me the parameters you set? Thanks a lot!
from alignedreid-re-production-pytorch.
@ZHHJemotion Do you mean GL + TWGD
87.05%
vs GL + LL + TWGALD
88.18%
in the Train on Market1501
sheet of AlignedReID-Scores.xlsx
? If you refer to this, then it is simply:
python script/experiment/train.py \
-d '(0,)' \
-r 1 \
--dataset market1501 \
--ids_per_batch 32 \
--ims_per_id 4 \
--normalize_feature false \
-gm 0.3 \
-glw 1 \
-llw 0 \
-idlw 0 \
--base_lr 2e-4 \
--lr_decay_type exp \
--exp_decay_at_epoch 151 \
--total_epochs 300
vs.
python script/experiment/train.py \
-d '(0,)' \
-r 1 \
--dataset market1501 \
--ids_per_batch 32 \
--ims_per_id 4 \
--normalize_feature false \
--local_dist_own_hard_sample true \
-gm 0.3 \
-glw 1 \
-llw 1 \
-idlw 0 \
--base_lr 2e-4 \
--lr_decay_type exp \
--exp_decay_at_epoch 151 \
--total_epochs 300
from alignedreid-re-production-pytorch.
@huanghoujing Yes. It is that! And I have the last one question: for with mutual learning, would it improve ~1 points with local distance than without local distance? is "~1 point improvement" also suitable for CUHK03 and Duke? But my experiemnt on CUHK03 and Duke didn't get ~1 point improvement, adding local distance shows similar with that without local distance. Thanks!
from alignedreid-re-production-pytorch.
@ZHHJemotion In my provided scores, when mutual loss is used, with and without local distance make no much difference.
from alignedreid-re-production-pytorch.
Hi houjing! Why does ldm_loss work only if the local_dist_own_hard_sample is true? The paper mentioned they trained the network with both global and local loss, and only use global features in the inference stage.
from alignedreid-re-production-pytorch.
GL + LL + TWGALD
:
python script/experiment/train.py
-d '(0,)'
-r 1
--dataset market1501
--ids_per_batch 32
--ims_per_id 4
--normalize_feature false
--local_dist_own_hard_sample true
-gm 0.3
-glw 1
-llw 1
-idlw 0
--base_lr 2e-4
--lr_decay_type exp
--exp_decay_at_epoch 151
--total_epochs 300
Hi @huanghoujing , If I want to train with both global and local distance loss, how do I set glw and llw? According to above script parametres, what does "glw=1 and llw=1" mean? Why does it not make
"glw+llw=1"? For example, glw=0.5 and llw=0.5.
from alignedreid-re-production-pytorch.
Related Issues (20)
- top-k结果可视化 HOT 2
- CUHK03和DUKE上的识别率 HOT 1
- How to inference my own test set
- Keys not found in source state_dict HOT 1
- Global Feature Extraction HOT 1
- Local feature dimensions
- About performance on market1501 for global learning and mutual learning
- Is it generalised
- TypeError: __init__() got an unexpected keyword argument 'log_dir'
- AssertionError HOT 3
- 请问论文中的Resnet50-Xception结构是不是没有实现? HOT 1
- 论文复现的参数问题
- how to use the test data to draw picture just like roc missrate cmc?
- How to use without GPU? HOT 2
- could you send me a partitions.pkl about market1501 HOT 1
- 为啥用你提供的weight测得market也只有88.78的top1呢 HOT 1
- how to infer some images or videos
- RuntimeError: cannot perform reduction function min on tensor with no elements because the operation does not have an identity
- Poor performnace when reproducing evaluation on market1501 HOT 2
- would you help me to fix this error?
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