JDet

Introduction

JDet is an object detection benchmark based on Jittor, and mainly focus on aerial image object detection (oriented object detection).

Install

JDet environment requirements:

• System: Linux(e.g. Ubuntu/CentOS/Arch), macOS, or Windows Subsystem of Linux (WSL)
• Python version >= 3.7
• CPU compiler (require at least one of the following)
  • g++ (>=5.4.0)
  • clang (>=8.0)
• GPU compiler (optional)
  • nvcc (>=10.0 for g++ or >=10.2 for clang)
• GPU library: cudnn-dev (recommend tar file installation, reference link)

Step 1: Install the requirements

git clone https://github.com/Jittor/JDet
cd JDet
python -m pip install -r requirements.txt

If you have any installation problems for Jittor, please refer to Jittor

Step 2: Install JDet

cd JDet
# suggest this 
python setup.py develop
# or
python setup.py install

If you don't have permission for install,please add --user.

Or use PYTHONPATH: You can add export PYTHONPATH=$PYTHONPATH:{you_own_path}/JDet/python into .bashrc, and run

source .bashrc

Getting Started

Datasets

The following datasets are supported in JDet, please check the corresponding document before use.

DOTA1.0/DOTA1.5/DOTA2.0 Dataset: dota.md.

FAIR Dataset: fair.md

SSDD/SSDD+: ssdd.md

You can also build your own dataset by convert your datas to DOTA format.

Config

JDet defines the used model, dataset and training/testing method by config-file, please check the config.md to learn how it works.

Train

python tools/run_net.py --config-file=configs/s2anet_r50_fpn_1x_dota.py --task=train

Test

If you want to test the downloaded trained models, please set resume_path={you_checkpointspath} in the last line of the config file.

python tools/run_net.py --config-file=configs/s2anet_r50_fpn_1x_dota.py --task=test

Test on images / Visualization

You can test and visualize results on your own image sets by:

python tools/run_net.py --config-file=configs/s2anet_r50_fpn_1x_dota.py --task=vis_test

You can choose the visualization style you prefer, for more details about visualization, please refer to visualization.md.

Build a New Project

In this section, we will introduce how to build a new project(model) with JDet. We need to install JDet first, and build a new project by:

mkdir $PROJECT_PATH$
cd $PROJECT_PATH$
cp $JDet_PATH$/tools/run_net.py ./
mkdir configs

Then we can build and edit configs/base.py like $JDet_PATH$/configs/retinanet.py. If we need to use a new layer, we can define this layer at $PROJECT_PATH$/layers.py and import layers.py in $PROJECT_PATH$/run_net.py, then we can use this layer in config files. Then we can train/test this model by:

python run_net.py --config-file=configs/base.py --task=train
python run_net.py --config-file=configs/base.py --task=test

Models

Notice:

1. ms: multiscale
2. flip: random flip
3. ra: rotate aug
4. ra90: rotate aug with angle 90,180,270
5. 1x : 12 epochs
6. bc: balance category
7. mAP: mean Average Precision on DOTA1.0 test set

Plan of Models

:heavy_check_mark:Supported :clock3:Doing :heavy_plus_sign:TODO

• :heavy_check_mark: S2ANet
• :heavy_check_mark: Gliding
• :heavy_check_mark: RetinaNet
• :heavy_check_mark: Rotated RetinaNet
• :heavy_check_mark: Faster R-CNN
• :heavy_check_mark: SSD
• :heavy_check_mark: ROI Transformer
• :heavy_check_mark: FCOS
• :heavy_check_mark: Oriented R-CNN
• :heavy_check_mark: YOLOv5
• :heavy_check_mark: GWD
• :heavy_check_mark: KLD
• :heavy_check_mark: H2RBox
• :heavy_check_mark: KFIoU
• :heavy_check_mark: Localization Distillation
• :heavy_check_mark: ReDet
• :heavy_check_mark: CSL
• :heavy_check_mark: Reppoints
• :heavy_check_mark: RSDet
• :heavy_check_mark: ATSS
• :clock3: R3Det
• :clock3: Cascade R-CNN
• :clock3: Oriented Reppoints
• :heavy_plus_sign: DCL
• :heavy_plus_sign: Double Head OBB
• :heavy_plus_sign: Guided Anchoring
• :heavy_plus_sign: ...

Plan of Datasets

:heavy_check_mark:Supported :clock3:Doing :heavy_plus_sign:TODO

• :heavy_check_mark: DOTA1.0
• :heavy_check_mark: DOTA1.5
• :heavy_check_mark: DOTA2.0
• :heavy_check_mark: SSDD
• :heavy_check_mark: SSDD+
• :heavy_check_mark: FAIR
• :heavy_check_mark: COCO
• :heavy_plus_sign: LS-SSDD
• :heavy_plus_sign: DIOR-R
• :heavy_plus_sign: HRSC2016
• :heavy_plus_sign: ICDAR2015
• :heavy_plus_sign: ICDAR2017 MLT
• :heavy_plus_sign: UCAS-AOD
• :heavy_plus_sign: FDDB
• :heavy_plus_sign: OHD-SJTU
• :heavy_plus_sign: MSRA-TD500
• :heavy_plus_sign: Total-Text
• :heavy_plus_sign: ...

Contact Us

Website: http://cg.cs.tsinghua.edu.cn/jittor/

Email: [email protected]

File an issue: https://github.com/Jittor/jittor/issues

QQ Group: 761222083

The Team

JDet is currently maintained by the Tsinghua CSCG Group. If you are also interested in JDet and want to improve it, Please join us!

Citation

@article{hu2020jittor,
  title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},
  author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},
  journal={Science China Information Sciences},
  volume={63},
  number={222103},
  pages={1--21},
  year={2020}
}

Reference

1. Jittor
2. mmrotate
3. Detectron2
4. mmdetection
5. maskrcnn_benchmark
6. RotationDetection
7. s2anet
8. gliding_vertex
9. oriented_rcnn
10. r3det
11. AerialDetection
12. DOTA_devkit
13. OBBDetection