Small Vehicle Detector

Requirements

• Python 2.7.+

• TensorFlow v1.+

• OpenCV

Install

1. Clone the repository

git clone https://github.com/HCMUS-Smart-Environment-Group/small-vehicle-detector

2. Update your -arch in setup script to match your GPU

cd tf-faster-rcnn/lib
# Change the GPU architecture (-arch) if necessary
vim setup.py

GPU model	Architecture
TitanX (Maxwell/Pascal)	sm_52
GTX 960M	sm_50
GTX 1080 (Ti)	sm_61
Grid K520 (AWS g2.2xlarge)	sm_30
Tesla K80 (AWS p2.xlarge)	sm_37

Note: You are welcome to contribute the settings on your end if you have made the code work properly on other GPUs. Also even if you are only using CPU tensorflow, GPU based code (for NMS) will be used by default, so please set USE_GPU_NMS False to get the correct output.

3. Build the Cython modules

make clean
make
cd ..

4. Install the Python COCO API. The code requires the API to access COCO dataset.

cd data
git clone https://github.com/pdollar/coco.git
cd coco/PythonAPI
make
cd ../../..

Download Pretrain Model

Link

Request Access is required. Contact email smartenvironment@selab.hcmus.edu.vn

Create mall-vehicle-detector/output/res101 folder

After extracting file, copy those files to small-vehicle-detector/output/res101

Demo

python tools/demo.py