CornerNet

tensorflow

CornerNet: Training and Evaluation Code

Code for reproducing the results in the following paper:

CornerNet: Detecting Objects as Paired Keypoints
Hei Law, Jia Deng
European Conference on Computer Vision (ECCV), 2018

Getting Started

environment

tensorflow==1.10
python3.6

Our current implementation only supports GPU so you need a GPU and need to have CUDA installed on your machine.

Installing MS COCO APIs

You also need to install the MS COCO APIs.

cd <CornetNet dir>/data
git clone https://github.com/cocodataset/cocoapi.git 
cd <CornetNet dir>/data/coco/PythonAPI
make

Downloading MS COCO Data

• Download the training/validation split we use in our paper from here (originally from Faster R-CNN)
• Unzip the file and place annotations under <CornetNet dir>/data/coco
• Download the images (2014 Train, 2014 Val, 2017 Test) from here
• Create 3 directories, trainval2014, minival2014 and testdev2017, under <CornerNet dir>/data/coco/images/
• Copy the training/validation/testing images to the corresponding directories according to the annotation files

Training and Evaluation

We provide the configuration file (CornerNet.json) and the model file (CornerNet.py) for CornerNet in this repo.

To train CornerNet:

python train.py

To use the trained model:

python test.py 

##In the next few days I will provide model parameters that are trained on coco.