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

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

Datasets

• veri776
• vehicleID

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

model:resnet50

loss: xent+htri

testset size	mAP	rank-1	rank-5	rank-20
800	76.4	69.1	85.8	94.5
1600	74.1	67.4	80.5	90.5
2400	71.4	65.2	78.3	89.2