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Person ReID

The codes are expanded on a ReID-baseline

Bag of tricks

• Warm up learning rate
• Random erasing augmentation
• Label smoothing
• Last stride

Loss function

1. "ranked_loss", SoftMax(LS) + w*RLL, results as "Results". For RLL I use Ranked List Loss for Deep Metric Learning, and made some modifications.

2. "cranked_loss", SoftMax(LS) + w*RLL(kmeans), before compute RLL i use kemeans to cluster features to help find hard samples. Now I can only get same performance to ranked_loss, so not report results.

Pipeline

Results

models

NOTE: For the the limitations of the device (GTX1060 6G), while training Se_ResNet50 and Se_ResNext50 i can only set batchsize = 48, others is 64.

Get Started

1. cd to folder where you want to download this repo

2. Run git clone https://github.com/Qidian213/Ranked_Person_ReID

3. Install dependencies:

   • pytorch>=0.4
   • torchvision
   • ignite=0.1.2 (Note: V0.2.0 may result in an error)
   • yacs

4. Prepare dataset

   Create a directory to store reid datasets under this repo or outside this repo. Remember to set your path to the root of the dataset in config/defaults.py for all training and testing or set in every single config file in configs/ or set in every single command.

   You can create a directory to store reid datasets under this repo via

   cd data
   

   （1）Market1501

   • Download dataset to data/ from http://www.liangzheng.org/Project/project_reid.html
   • Extract dataset and rename to market1501. The data structure would like:

   data
       market1501 # this folder contains 6 files.
           bounding_box_test/
           bounding_box_train/
           ......
   

   （2）DukeMTMC-reID

   • Download dataset to data/ from https://github.com/layumi/DukeMTMC-reID_evaluation#download-dataset
   • Extract dataset and rename to dukemtmc-reid. The data structure would like:

   data
       dukemtmc-reid
       	DukeMTMC-reID # this folder contains 8 files.
           	bounding_box_test/
           	bounding_box_train/
           	......
   

5. Prepare pretrained model if you don't have

   （1）ResNet

   from torchvision import models
   models.resnet50(pretrained=True)
   

   （2）Senet

   import torch.utils.model_zoo as model_zoo
   model_zoo.load_url('the pth you want to download (specific urls are listed in  ./modeling/backbones/senet.py)')
   

   Then it will automatically download model in ~/.torch/models/, you should set this path in config/defaults.py for all training or set in every single training config file in configs/ or set in every single command.

   （3）ResNet_IBN_a , faster implementation

   You can download from here [link]

   （4）Load your self-trained model If you want to continue your train process based on your self-trained model, you can change the configuration PRETRAIN_CHOICE from 'imagenet' to 'self' and set the PRETRAIN_PATH to your self-trained model.

6. If you want to know the detailed configurations and their meaning, please refer to config/defaults.py. If you want to set your own parameters, you can follow our method: create a new yml file, then set your own parameters. Add --config_file='configs/your yml file' int the commands described below, then our code will merge your configuration. automatically.

Train

1. Market1501

python tools/train.py --config_file='configs/softmax_ranked.yml' DATASETS.NAMES "('market1501')" 

2. DukeMTMC-reID

python tools/train.py --config_file='configs/softmax_ranked.yml' DATASETS.NAMES "('dukemtmc')" 

Test

python tools/test.py --config_file='configs/softmax_ranked.yml' MODEL.DEVICE_ID "('your device id')" DATASETS.NAMES "('market1501')" TEST.FEAT_NORM "('yes')" TEST.RE_RANKING "('no')" MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('your path to trained checkpoints')"

Using and Compute threshold of negative and postive samples

1. DukeMTMC-reID

2. Market1501

Using

python tools/demo.py 

Compute threshold , to compute threshold i use the hardest samples of negative and postive samples in a batchsizes.

python tools/compute_threshold.py --config_file='configs/softmax_ranked.yml' MODEL.PRETRAIN_CHOICE "('self')"  DATASETS.NAMES "('market1501')" TEST.WEIGHT "('models/resnet50_ibn_a/mar_resnet50_ibn_a_model.pth')"