HAT: Hierarchical Aggregation Transformers for Person Re-identification

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Deep Learning HAT
Overview

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This Repo. is used for our ACM MM2021 paper:

HAT: Hierarchical Aggregation Transformers for Person Re-identification

(https://arxiv.org/pdf/2107.05946.pdf)

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For using the codes, please download the public Market-1501 and Duke datasets. Besides, please install the Pytorch as the Official Suggestions.

Then, run the train.py and test.py with your GPUs. You can get the same results in the above paper.

The codes can be download at

Link：https://pan.baidu.com/s/10wt1ai0-zza7RohkIs60IQ

Extracting code：4ox2

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If you have any problems. Please concat

[email protected] or [email protected]

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Comments
  • About the download link

    About the download link

    Hello,

    Thank you for your work. I tried to follow your instruction on downloading the code, but the Baidu site required login in order to download the code, and I do not have a Baidu account. Could you please upload the files to other cloud service or github?

    Thank you and best regards.

    opened by viet2411 0
  • ValueError: expected 2D or 3D input (got 1D input)

    ValueError: expected 2D or 3D input (got 1D input)

    Hi~

    Thanks for releasing code. Would be better if you could answer the following question.

    I found that training process will stop after epoch30 with an error " ValueError: expected 2D or 3D input (got 1D input)", could you please help me to solve this problem? ( I have replaced duke dataset with my own dataset, and set the batch size to 32 due to GPU limitations).

    Looking forward to your kindly reply.

    Best, Yarloy

    opened by Yarloy 1
  • the training procedure crash when input is list of tensor

    the training procedure crash when input is list of tensor

    when train the HAT, i meet this problem

     File "/home/tiger/hand-gestures/3rd/HAT/engine/trainer.py", line 46, in _update
   loss = loss_fn(score, feat, target)
 File "/home/tiger/hand-gestures/3rd/HAT/layers/__init__.py", line 40, in loss_func
   return F.cross_entropy(score, target) + triplet(feat, target)[0]
 File "/home/tiger/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 2468, in cross_entropy
   return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
 File "/home/tiger/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 1605, in log_softmax
   ret = input.log_softmax(dim)
AttributeError: 'list' object has no attribute 'log_softmax'

    any idea to solve it? thanks

    opened by zimenglan-sysu-512 2
Owner
I am currently an Associate Professor with the School of Artificial Intelligence, Dalian University of Technology.
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