Experiment with Centroid Re-ID by Changing Backbone Using EfficientNet-V2 and Adding Re-Identification Based Data Augmentation
├── config
│ └── defaults.py - here's the default config file.
│
│
├── configs
│ └── train_mnist_softmax.yml - here's the specific config file for specific model or dataset.
│
│
├── data
│ └── datasets - here's the datasets folder that is responsible for all data handling.
│ └── transforms - here's the data preprocess folder that is responsible for all data augmentation.
│ └── build.py - here's the file to make dataloader.
│ └── collate_batch.py - here's the file that is responsible for merges a list of samples to form a mini-batch.
│
│
├── engine
│ ├── trainer.py - this file contains the train loops.
│ └── inference.py - this file contains the inference process.
│
│
├── layers - this folder contains any customed layers of your project.
│ └── conv_layer.py
│
│
├── modeling - this folder contains any model of your project.
│ └── example_model.py
│
│
├── solver - this folder contains optimizer of your project.
│ └── build.py
│ └── lr_scheduler.py
│
│
├── tools - here's the train/test model of your project.
│ └── train_net.py - here's an example of train model that is responsible for the whole pipeline.
│
│
└── utils
│ ├── logger.py
│ └── any_other_utils_you_need
│
│
└── tests - this foler contains unit test of your project.
├── test_data_sampler.py
Any kind of enhancement or contribution is welcomed.
@article{Wieczorek2021OnTU,
title={On the Unreasonable Effectiveness of Centroids in Image Retrieval},
author={Mikolaj Wieczorek and Barbara Rychalska and Jacek Dabrowski},
journal={ArXiv},
year={2021},
volume={abs/2104.13643}
}
@article{ReIDAugmentations,
title={A Person Re-identification Data Augmentation Method with Adversarial Defense Effect},
author={Yunpeng Gong and Zhiyong Zeng and Liwen Chen and Yifan Luo and Bin Weng and Feng Ye},
year={2021},
journal={ArXiv}
eprint={2101.08783},
}