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deep-efficient-reid

Experiment for an uni project with strong baseline for Efficientnet-v2

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