Zhedong Zheng

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mkl version may be wrong. You may re-install your mkl.

@wangsun1996 对的,可以优化一下效率。比如一个tracklet就留几张图就行。

It is added at https://github.com/layumi/Person_reID_baseline_pytorch/blob/master/random_erasing.py#L57

Hi @Basavarajdocketrun When evaluation, you could use the cosine distance as well. Just extract the new feature of the same person in the new camera and do the comparison. By...

Hi @mcjqwer The original paper split the feature into 6 parts, which equals to use [6] adaptive pooling.

You may refer to https://github.com/layumi/Person-reID-triplet-loss

Hi @tjusyy 可能是 timm版本问题。 更新timm就行。 Please update your timm and try it again

https://github.com/layumi/Person_reID_baseline_pytorch/blob/master/model.py#L121 要更新到 0.6.0以上。timm 改写法了。 ``` pip install git+https://github.com/rwightman/pytorch-image-models.git ```

Sure. Recently, I also applied it to vehicle retrieval (re-id) competition, yielding the 1st place @clw5180 . You may check more details at https://github.com/layumi/AICIty-reID-2020.