Open-ReID-tracking

This repo is based on Cysu's open-reid, which is a great re-ID library. For performance, we implemented some other baseline models on top of it. For utility, we add some function for the tracking-by-detection workflow in tracking works.

• update all models for performance & readability.
• add data/README.md. check for folder structure & dataset download.
• add requirements.txt. use conda install --file requirements.txt to install.
• add BN after feature layer in reid/models/IDE_model.py for separation. This introduces a higher performance.
• fix high cpu usage via adding os.environ['OMP_NUM_THREADS'] = '1' in runable files.
• NEW: We adopt a baseline from Hao Luo [git, paper]. See ZJU.py. We achieve competitive performance with the same IDE_model.py.

Please use this repo alongside with our flavor of DeepCC tracker for tracking.

Model

• IDE [paper]
• Triplet [paper]
• PCB [git, paper]
• ZJU [git, paper]

Data

The re-ID datasets should be stored in a file structure like this:

~
└───Data
    └───AIC19
    │   │ track-1 data
    │   │ ...
    │
    └───AIC19-reid
    │   │ track-2 data
    │   │ ...
    │
    └───VeRi
    │   │ ...
    │
    └───DukeMTMC-reID
    │   │ ...
    │
    └───Market-1501-v15.09.15
        │ ...

Usage

Re-ID

training from scratch

CUDA_VISIBLE_DEVICES=0 python3 IDE.py -d market1501 --train

this will automatically save your logs at ./logs/ide/market1501/YYYY-MM-DD_HH-MM-SS, where YYYY-MM-DD_HH-MM-SS is the time stamp when the training started.

resume & evaluate

CUDA_VISIBLE_DEVICES=0 python3 IDE.py -d market1501 --resume YYYY-MM-DD_HH-MM-SS

Feature Extraction for Tracking (to be updated)

We describe the workflow for a simple model. For the full ensemble model, please check

First, please use the following to extract detection bounding boxes from videos.

python3 reid/prepare/extract_bbox.py

Next, train the baseline on re-ID data from AI-City 2019 (track-2).

# train
CUDA_VISIBLE_DEVICES=0,1 python3 ZJU.py --train -d aic_reid --logs-dir logs/ZJU/256/aic_reid/lr001_colorjitter --colorjitter  --height 256 --width 256 --lr 0.01 --step-size 30,60,80 --warmup 10 --LSR --backbone densenet121 --features 256 --BNneck -s 1 -b 64 --epochs 120

Then, the detection bounding box feature are computed.

# gt feat (optional)
# CUDA_VISIBLE_DEVICES=0,1 python3 save_cnn_feature.py -a zju --backbone densenet121 --resume logs/ZJU/256/aic_reid/lr001_colorjitter/model_best.pth.tar --features 256 --height 256 --width 256 --l0_name zju_lr001_colorjitter_256 --BNneck -s 1 -d aic --type gt_all -b 64
# reid feat (parameter tuning, see DeepCC_aic)
CUDA_VISIBLE_DEVICES=0,1 python3 save_cnn_feature.py -a zju --backbone densenet121 --resume logs/ZJU/256/aic_reid/lr001_colorjitter/model_best.pth.tar --features 256 --height 256 --width 256 --l0_name zju_lr001_colorjitter_256 --BNneck -s 1 -d aic --type gt_mini -b 64
# det feat (tracking pre-requisite, see DeepCC_aic)
CUDA_VISIBLE_DEVICES=0,1 python3 save_cnn_feature.py -a zju --backbone densenet121 --resume logs/ZJU/256/aic_reid/lr001_colorjitter/model_best.pth.tar --features 256 --height 256 --width 256 --l0_name zju_lr001_colorjitter_256 --BNneck -s 1 -d aic --type detections --det_time trainval -b 64
CUDA_VISIBLE_DEVICES=0,1 python3 save_cnn_feature.py -a zju --backbone densenet121 --resume logs/ZJU/256/aic_reid/lr001_colorjitter/model_best.pth.tar --features 256 --height 256 --width 256 --l0_name zju_lr001_colorjitter_256 --BNneck -s 1 -d aic --type detections --det_time test -b 64

Implementation details

Cross-entropy loss:

• batch_size = 64.
• learning rate = 0.1, step decay after 40 epochs. Train for 60 epochs in total.
• 0.1x learning rate for resnet-50 base.
• weight decay = 5e-4.
• SGD optimizer, momentum = 0.9, nestrov = true.

Triplet loss:

• margin=0.3.
• ims_per_id = 4, ids_per_batch = 32.
• learning rate = 2e-4, exponentially decay after 150 epochs. Train for 300 epochs in total.
• unifide learning rate for resnet-50 base and fc feature layer.
• weight decay = 5e-4.
• Adam optimizer.

Default Settings:

• IDE
  • stride = 2 in last conv block.
  • h x w = 256 x 128.
  • random horizontal flip + random crop.
• Triplet
  • stride = 2 in last conv block.
  • h x w = 256 x 128.
  • random horizontal flip + random crop.
• PCB
  • stride = 1 in last conv block.
  • h x w = 384 x 128.
  • random horizontal flip.
• ZJU
  • cross entropy + triplet.
  • ims_per_id = 4, ids_per_batch = 16.
  • h x w = 256 x 128.
  • warmup for 10 epochs.
  • random horizontal flip + pad 10 pixel then random crop + random erasing with re = 0.5.
  • label smooth.
  • stride = 1 in last conv block.
  • BNneck.
  • center loss.

Tracking settings for IDE, Triplet, and PCB:

• stride = 1 in last conv block.
• h x w = 384 x 128.
• horizontal flipping + Random Erasing with re = 0.5.

Raw setting for ZJU:

• cross entropy + triplet.
• ims_per_id = 4, ids_per_batch = 16.
• h x w = 256 x 128.
• random horizontal flip + pad 10 pixel then random crop.

Experiment Results