by Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang and Qi Tian
The code to train and eval our CenterNet is available here. Thanks Princeton Vision & Learning Lab! For more details, please refer to our arXiv paper.
Our method is an one-stage detector and learns from scratch. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%！
CenterNet is a framework for object detection with deep ConvNets. You can use the code to train/evaluate a network for object detection task.
- It achieves state-of-the-art performance on one of the most challenging dataset: MS-COCO.
- Our code is written by Python, based on CornerNet.
In terms of speed, we test the inference speed of both CornerNet and CenterNet on a NVIDIA Tesla P100 GPU. We obtain that the average inference time of CornerNet511-104 (means that the resolution of input images is 511X511 and the backbone is Hourglass-104) is 300ms per image and that of CenterNet511-104 is 340ms. Meanwhile, using the Hourglass-52 backbone can speed up the inference speed. Our CenterNet511-52 takes an average of 270ms to process per image, which is faster and more accurate than CornerNet511-104.
Please first install Anaconda and create an Anaconda environment using the provided package list.
conda create --name CenterNet --file conda_packagelist.txt
After you create the environment, activate it.
source activate CenterNet
cd <CenterNet dir>/models/py_utils/_cpools/
python setup.py install --user
cd <CenterNet dir>/external
make
cd <CenterNet dir>/data/coco/PythonAPI
make
- Download the training/validation split we use in our paper from here (originally from Faster R-CNN)
- Unzip the file and place
annotationsunder<CenterNet dir>/data/coco - Download the images (2014 Train, 2014 Val, 2017 Test) from here
- Create 3 directories,
trainval2014,minival2014andtestdev2017, under<CenterNet dir>/data/coco/images/ - Copy the training/validation/testing images to the corresponding directories according to the annotation files
To train CenterNet-104:
python train.py CenterNet-104
We provide the configuration file (CenterNet-104.json) and the model file (CenterNet-104.py) for CenterNet in this repo.
We also provide a trained model for CenterNet-104, which is trained for 480k iterations using 8 Tesla V100 (32GB) GPUs. You can download it from [here](https://pan.baidu.com/s/17RvbWaxrvW1kXRuk7XmIfw code:2clj) and put it under <CenterNet dir>/cache/nnet (You may need to create this directory by yourself if it does not exist). If you want to train you own CenterNet, please adjust the batch size in CenterNet-104.json to accommodate the number of GPUs that are available to you.
To use the trained model:
python test.py CenterNet-104 --testiter 480000 --split <split>
To train CenterNet-52:
python train.py CenterNet-52
We provide the configuration file (CenterNet-52.json) and the model file (CenterNet-52.py) for CenterNet in this repo.
We also provide a trained model for CenterNet-52, which is trained for 480k iterations using 8 Tesla V100 (32GB) GPUs. You can download it from [here](https://pan.baidu.com/s/1Ltig0csUPp4T5HA4BjHikA code:ed0y) and put it under <CenterNet dir>/cache/nnet (You may need to create this directory by yourself if it does not exist). If you want to train you own CenterNet, please adjust the batch size in CenterNet-52.json to accommodate the number of GPUs that are available to you.
To use the trained model:
python test.py CenterNet-52 --testiter 480000 --split <split>
We also include a configuration file for multi-scale evaluation, which is CenterNet-104-multi_scale.json and CenterNet-52-multi_scale.json in this repo, respectively.
To use the multi-scale configuration file:
python test.py CenterNet-52 --testiter <iter> --split <split> --suffix multi_scale
or
python test.py CenterNet-104 --testiter <iter> --split <split> --suffix multi_scale