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I have gone through both Joint discriminative and generative learning for person re-identification and A discriminatively learned CNN embedding for person re-identification, but neither of them reports a lot of the newer trained models scores (e.g. swin transformer, HRNet-18, PCB).
As far as I understand, the models provided in here are all based on the same architecture and what changes is the backbone and some parameters. For example:
- all tricks means ->
--warm_epoch 5 --stride 1 --erasing_p 0.5 --batchsize 8 --lr 0.02- circle loss
- DG
My questions are:
- Are there any other significant changes when implementing swin transformer or other backbones?
- Should I use Verif-Identif [55] as model name when citing the results of this repository, regardless of the architecture? Or is there a specific notation used when citing the models from this repo?
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