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
Please first install Anaconda and create an Anaconda environment using the provided package list.
conda create --name CornerNet --file conda_packagelist.txt
After you create the environment, activate it.
source activate CornerNet
Our current implementation only supports GPU so you need a GPU and need to have CUDA installed on your machine.
Compiling Corner Pooling Layers
You need to compile the C++ implementation of corner pooling layers.
cd <CornetNet dir>/models/py_utils/_cpools/ python setup.py install --user
cd <CornetNet dir>/external make
Installing MS COCO APIs
You also need to install the MS COCO APIs.
cd <CornetNet dir>/data git clone firstname.lastname@example.org:cocodataset/cocoapi.git coco 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
- Download the images (2014 Train, 2014 Val, 2017 Test) from here
- Create 3 directories,
- Copy the training/validation/testing images to the corresponding directories according to the annotation files
Training and Evaluation
To train and evaluate a network, you will need to create a configuration file, which defines the hyperparameters, and a model file, which defines the network architecture. The configuration file should be in JSON format and placed in
config/. Each configuration file should have a corresponding model file in
models/. i.e. If there is a
config/, there should be a
models/. There is only one exception which we will mention later.
To train a model:
python train.py <model>
We provide the configuration file (
CornerNet.json) and the model file (
CornerNet.py) for CornerNet in this repo.
To train CornerNet:
python train.py CornerNet
We also provide a trained model for
CornerNet, which is trained for 500k iterations using 10 Titan X (PASCAL) GPUs. You can download it from here and put it under
<CornerNet dir>/cache/nnet/CornerNet (You may need to create this directory by yourself if it does not exist). If you want to train you own CornerNet, please adjust the batch size in
CornerNet.json to accommodate the number of GPUs that are available to you.
To use the trained model:
python test.py CornerNet --testiter 500000 --split <split>
If you want to test different hyperparameters in testing and do not want to overwrite the original configuration file, you can do so by creating a configuration file with a suffix (
<model>-<suffix>.json). You DO NOT need to create
To use the new configuration file:
python test.py <model> --testiter <iter> --split <split> --suffix <suffix>
We also include a configuration file for multi-scale evaluation, which is
CornerNet-multi_scale.json, in this repo.
To use the multi-scale configuration file:
python test.py CornerNet --testiter <iter> --split <split> --suffix multi_scale