Experiment about Deep Person Re-identification with EfficientNet-v2

Overview

deep-efficient-person-reid

Experiment for an uni project with strong baseline for Person Re-identification task.

We evaluated the baseline with Resnet50 and Efficienet-v2 without using pretrained models. Also Resnet50-IBN-A and Efficientnet-v2 using pretrained on ImageNet. We used two datasets: Market-1501 and CUHK03.


Pipeline

pipeline


Implementation Details

  • Random Erasing to transform input images.
  • EfficientNet-v2 / Resnet50 / Resnet50-IBN-A as backbone.
  • Stride = 1 for last convolution layer. Embedding size for Resnet50 / Resnet50-IBN-A is 2048, while for EfficientNet-v2 is 1280. During inference, embedding features will run through a batch norm layer, as known as a bottleneck for better normalization.
  • Loss function combining 3 losses:
    1. Triplet Loss with Hard Example Mining.
    2. Classification Loss (Cross Entropy) with Label Smoothing.
    3. Centroid Loss - Center Loss for reducing the distance of embeddings to its class center. When combining it with Classification Loss, it helps preventing embeddings from collapsing.
  • The default optimizer is AMSgrad with base learning rate of 3.5e-4 and multistep learning rate scheduler, decayed at epoch 30th and epoch 55th. Besides, we also apply mixed precision in training.
  • In both datasets, pretrained models were trained for 60 epochs and non-pretrained models were trained for 100 epochs.

Source Structure

.
├── config                  # hyperparameters settings
│   └── ...                 # yaml files
├
├── datasets                # data loader
│   └── ...           
├
├── market1501              # market-1501 dataset
|
├── cuhk03_release          # cuhk03 dataset
|
├── samplers                # random samplers
│   └── ...
|
├── loggers                 # test weights and visualization results      
|   └── runs
|   
├── losses                  # loss functions
│   └── ...   
|
├── nets                    # models
│   └── bacbones            
│       └── ... 
│   
├── engine                  # training and testing procedures
│   └── ...    
|
├── metrics                 # mAP and re-ranking
│   └── ...   
|
├── utils                   # wrapper and util functions 
│   └── ...
|
├── train.py                # train code 
|
├── test.py                 # test code 
|
├── visualize.py            # visualize results 

Pretrained Models (on ImageNet)

  • EfficientNet-v2: link
  • Resnet50-IBN-A: link

Notebook

  • Notebook to train, inference and visualize: Notebook

Setup


  • Install dependencies, change directory to dertorch:
pip install -r requirements.txt
cd dertorch/

  • Modify config files in /configs/. You can play with the parameters for better training, testing.

  • Training:
python train.py --config_file=name_of_config_file
Ex: python train.py --config_file=efficientnetv2_market

  • Testing: Save in /loggers/runs, for example the result from EfficientNet-v2 (Market-1501): link
python test.py --config_file=name_of_config_file
Ex: python test.py --config_file=efficientnetv2_market

  • Visualization: Save in /loggers/runs/results/, for example the result from EfficienNet-v2 (Market-1501): link
python visualize.py --config_file=name_of_config_file
Ex: python visualize.py --config_file=efficientnetv2_market

Examples


Query image 1 query1


Result image 1 result1


Query image 2 query2


Result image 2 result2


Results

  • Market-1501
Models Image Size mAP Rank-1 Rank-5 Rank-10 weights
Resnet50 (non-pretrained) 256x128 51.8 74.0 88.2 93.0 link
EfficientNet-v2 (non-pretrained) 256x128 56.5 78.5 91.1 94.4 link
Resnet50-IBN-A 256x128 77.1 90.7 97.0 98.4 link
EfficientNet-v2 256x128 69.7 87.1 95.3 97.2 link
Resnet50-IBN-A + Re-ranking 256x128 89.8 92.1 96.5 97.7 link
EfficientNet-v2 + Re-ranking 256x128 85.6 89.9 94.7 96.2 link

  • CUHK03:
Models Image Size mAP Rank-1 Rank-5 Rank-10 weights
Resnet50 (non-pretrained) ... ... ... ... ... ...
EfficientNet-v2 (non-pretrained) 256x128 10.1 10.1 21.1 29.5 link
Resnet50-IBN-A 256x128 41.2 41.8 63.1 71.2 link
EfficientNet-v2 256x128 40.6 42.9 63.1 72.5 link
Resnet50-IBN-A + Re-ranking 256x128 55.6 51.2 64.0 72.0 link
EfficientNet-v2 + Re-ranking 256x128 56.0 51.4 64.7 73.4 link

The results from EfficientNet-v2 models might be better if fine-tuning properly and longer training epochs, while here we use the best parameters for the ResNet models (on Market-1501 dataset) from this paper and only trained for 60 - 100 epochs.


Citation

@article{DBLP:journals/corr/abs-2104-13643,
  author    = {Mikolaj Wieczorek and
               Barbara Rychalska and
               Jacek Dabrowski},
  title     = {On the Unreasonable Effectiveness of Centroids in Image Retrieval},
  journal   = {CoRR},
  volume    = {abs/2104.13643},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.13643},
  archivePrefix = {arXiv},
  eprint    = {2104.13643},
  timestamp = {Tue, 04 May 2021 15:12:43 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-13643.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@InProceedings{Luo_2019_CVPR_Workshops,
author = {Luo, Hao and Gu, Youzhi and Liao, Xingyu and Lai, Shenqi and Jiang, Wei},
title = {Bag of Tricks and a Strong Baseline for Deep Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

Adapted from: michuanhaohao

Comments
  • Download links for Pretrained weights not working

    Download links for Pretrained weights not working

    First of all, thank you for the amazing work!

    It seems that the links to download the ImageNet pre-trained models, datasets, and EfficientNet-v2 and Resnet50-IBN-A are not working, giving a 404 page not found error.

    opened by chandantarani 2
  • An error occurs ' 'ValueError: too many values to unpack (expected 3)'

    An error occurs ' 'ValueError: too many values to unpack (expected 3)'

    An error occurs ' 'ValueError: too many values to unpack (expected 3)'

    When I run a code 'python train.py --config_file=resnet50_cuhk', the train starts successfully but at the end, after 50 epoch, an error occurs like this 'ValueError: too many values to unpack (expected 3)' How can I solve this problem?

    opened by jhq1234 1
  • How to setup the folder structure?

    How to setup the folder structure?

    1. Could you please share in which folder the datasets and pre-trained weights should be downloaded and placed?

    2. Thanks for sharing the colab notebook (makes it very easy to test the code). Could you add the code to automatically download (wget) the datasets and pre-trained weights in the right folder in the notebook itself? Else it is quite confusing how to set up the directories to run the code.

    Thank you for sharing the code with the community. @lannguyen0910

    opened by ankurguria 1
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    tensorboard 2.1.0 requires protobuf, which is not installed.
    scikit-image 0.14.5 requires networkx, which is not installed.
    apex 0.9.10 requires cryptacular, which is not installed.
    albumentations 0.4.3 requires opencv-python-headless, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3180413 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

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    🦉 Regular Expression Denial of Service (ReDoS)

    opened by snyk-bot 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    tensorboard 2.1.0 requires protobuf, which is not installed.
    scikit-image 0.14.5 requires networkx, which is not installed.
    apex 0.9.10 requires cryptacular, which is not installed.
    albumentations 0.4.3 requires opencv-python-headless, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by lannguyen0910 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    zope.sqlalchemy 1.6 requires SQLAlchemy, which is not installed.
    velruse 1.1.1 requires anykeystore, which is not installed.
    tensorboard 2.1.0 requires protobuf, which is not installed.
    tensorboard 2.1.0 requires grpcio, which is not installed.
    apex 0.9.10 requires cryptacular, which is not installed.
    albumentations 0.4.3 requires imgaug, which is not installed.
    albumentations 0.4.3 requires opencv-python-headless, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by lannguyen0910 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    zope.sqlalchemy 1.6 requires zope.interface, which is not installed.
    zope.sqlalchemy 1.6 requires SQLAlchemy, which is not installed.
    transaction 3.0.1 requires zope.interface, which is not installed.
    tensorboard 2.1.0 requires protobuf, which is not installed.
    tensorboard 2.1.0 requires grpcio, which is not installed.
    tensorboard 2.1.0 requires numpy, which is not installed.
    tensorboard 2.1.0 has requirement setuptools>=41.0.0, but you have setuptools 39.0.1.
    scipy 1.2.0 requires numpy, which is not installed.
    scikit-image 0.14.5 requires networkx, which is not installed.
    PyWavelets 1.0.3 requires numpy, which is not installed.
    pyramid 1.10.8 requires zope.interface, which is not installed.
    pandas 0.24.2 requires numpy, which is not installed.
    matplotlib 2.2.5 requires numpy, which is not installed.
    imgaug 0.2.6 requires numpy, which is not installed.
    h5py 2.10.0 requires numpy, which is not installed.
    google-auth 1.35.0 has requirement setuptools>=40.3.0, but you have setuptools 39.0.1.
    apex 0.9.10 requires cryptacular, which is not installed.
    apex 0.9.10 requires pyramid-mailer, which is not installed.
    apex 0.9.10 requires wtforms-recaptcha, which is not installed.
    albumentations 0.4.3 requires numpy, which is not installed.
    albumentations 0.4.3 requires opencv-python-headless, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by lannguyen0910 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    ⚠️ Warning
    tensorboard 2.1.0 requires protobuf, which is not installed.
    tensorboard 2.1.0 requires grpcio, which is not installed.
    scikit-image 0.14.5 requires pillow, which is not installed.
    scikit-image 0.14.5 requires networkx, which is not installed.
    apex 0.9.10 requires cryptacular, which is not installed.
    apex 0.9.10 requires velruse, which is not installed.
    apex 0.9.10 requires pyramid-mailer, which is not installed.
    apex 0.9.10 requires wtforms-recaptcha, which is not installed.
    albumentations 0.4.3 requires opencv-python-headless, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: 🧐 View latest project report

    🛠 Adjust project settings

    📚 Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    🦉 Regular Expression Denial of Service (ReDoS)

    opened by lannguyen0910 0
  • training parameter

    training parameter

    Hi, I'm kazuki-can. Thanks for your attractive work. Since I'm going to write a paper about Computer Vision with this repository, I would like to know its training parameter. I use the model file called "efficienetv2_imagenet.pt".

    Thank you.

    opened by kazuki-can 0
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