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
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:
- Triplet Loss with Hard Example Mining.
- Classification Loss (Cross Entropy) with Label Smoothing.
- 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)
Notebook
- Notebook to train, inference and visualize:
Setup
- Download datasets: Market-1501 and CUHK03
- 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 
Result image 1 
Query image 2 
Result image 2 
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
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