JDet

Introduction

JDet is Object Detection Framework based on Jittor.

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/li-xl/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
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

source .bashrc

Getting Started

Data

DOTA Dataset documents are avaliable in the dota.md

FAIR Dataset documents are avaliable in the fair.md

Config

Config documents are avaliable in the config.md

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

Build a New Project

In this document, 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

Plan

✔️ Supported 🕒 Doing ➕ TODO

• ✔️ S2ANet
• ✔️ Gliding
• ✔️ RetinaNet
• ✔️ Faster R-CNN
• 🕒 SSD
• 🕒 ReDet
• 🕒 YOLOv5
• 🕒 R3Det
• 🕒 Cascade R-CNN
• 🕒 ROI Transformer
• ➕ CSL
• ➕ DCL
• ➕ GWD
• ➕ KLD
• ➕ ...

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. Detectron2
3. mmdetection
4. maskrcnn_benchmark
5. RotationDetection
6. s2anet
7. gliding_vertex
8. r3det
9. AerialDetection