doubleheadsrcnn
doubleheadsrcnn copied to clipboard
wuyuebupt
Rethinking Classification and Localization for Object Detection
Double Heads RCNN
This is the implementation of CVPR 2020 paper "Rethinking Classification and Localization for Object Detection". The code is based on the maskrcnn-benchmark.
If the paper and code helps you, we would appreciate your kindly citations of our paper.
@inproceedings{wu2020rethinking,
title={Rethinking Classification and Localization for Object Detection},
author={Wu, Yue and Chen, Yinpeng and Yuan, Lu and Liu, Zicheng and Wang, Lijuan and Li, Hongzhi and Fu, Yun},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2020}
}
Contents
- Installation
- Models
- Running
Installation
Follow the maskrcnn-benchmark to install code and set up the dataset.
A docker image is also provided
docker pull yuewudocker/pytorchdoubleheads
If you use this docker, you can run the ./cmd_install.sh script for the installation.
Most experiments are done under the following environments:
PyTorch version: 1.0.0
OS: Ubuntu 16.04.3 LTS
Python version: 3.6
CUDA runtime version: 9.0.176
Nvidia driver version: 410.78
GPU: 4x Tesla P100-PCIE-16GB
Models
Results on the COCO 2017 validation set:
| Backbone | AP | AP_0.5 | AP_0.7 | AP_s | AP_m | AP_l | Link |
|---|---|---|---|---|---|---|---|
| ResNet-50-FPN | 40.3 | 60.3 | 44.2 | 22.4 | 43.3 | 54.3 | model |
| ResNet-101-FPN | 41.9 | 62.4 | 45.9 | 23.9 | 45.2 | 55.8 | model |
Results on COCO 2017 test-dev:
| Backbone | AP | AP_0.5 | AP_0.7 | AP_s | AP_m | AP_l | Link |
|---|---|---|---|---|---|---|---|
| ResNet-101-FPN | 42.3 | 62.8 | 46.3 | 23.9 | 44.9 | 54.3 | bbox |
Running
Use config files in ./configs/double_heads/ for Training and Testing.
Run Inference
Download models to the ./models directory. Then use the following script:
sh cmd_test.sh
You need modify the data path:
export DATA_DIR=/path/to/datafolder/
Run Training
You can use the ./cmd_train.sh script to train with 4 gpus.
You have to modify following paths:
export OUTPUT_DIR=/path/to/modelfolder/
export PRETRAIN_MODEL=/path/to/pretrained/model
export DATA_DIR=/path/to/datafolder/
wuyuebupt