vehicle-ReID-baseline

Introduction

Vehicle ReID baseline is a pytorch-based baseline for training and evaluating deep vehicle re-identification models on reid benchmarks.

Updates

2019.4.1 update some test results

2019.3.11 update the basic baseline code

Installation

cd to your preferred directory and run ' git clone https://github.com/Jakel21/vehicle-ReID '.
Install dependencies by pip install -r requirements.txt (if necessary).

Datasets

The keys to use these datasets are enclosed in the parentheses. See vehiclereid/datasets/init.py for details.Both two datasets need to pull request to the supplier.

Models

resnet50

Losses

cross entropy loss
triplet loss

Tutorial

train

Input arguments for the training scripts are unified in args.py. To train an image-reid model with cross entropy loss, you can do

python train-xent-tri.py \
-s veri \    #source dataset for training
-t veri \    # target dataset for test
--height 128 \ # image height
--width 256 \ # image width
--optim amsgrad \ # optimizer
--lr 0.0003 \ # learning rate
--max-epoch 60 \ # maximum epoch to run
--stepsize 20 40 \ # stepsize for learning rate decay
--train-batch-size 64 \
--test-batch-size 100 \
-a resnet50 \ # network architecture
--save-dir log/resnet50-veri \ # where to save the log and models
--gpu-devices 0 \ # gpu device index

test

Use --evaluate to switch to the evaluation mode. In doing so, no model training is performed. For example you can load pretrained model weights at path_to_model.pth.tar on veri dataset and do evaluation on VehicleID, you can do

python train_imgreid_xent.py \
-s veri \ # this does not matter any more
-t vehicleID \ # you can add more datasets here for the test list
--height 128 \
--width 256 \
--test-size 800 \
--test-batch-size 100 \
--evaluate \
-a resnet50 \
--load-weights path_to_model.pth.tar \
--save-dir log/eval-veri-to-vehicleID \
--gpu-devices 0 \

Results

Some test results on veri776 and vehicleID:

veri776

model:resnet50

loss: xent+htri

mAP	rank-1	rank-5	rank-20
59.0	87.6	94.3	98.2

vehicleID