JDet is Object Detection Framework based on Jittor.


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.

You can add export PYTHONPATH=$PYTHONPATH:{you_own_path}/JDet/python into .bashrc

source .bashrc

Getting Started


DOTA Dataset documents are avaliable in the dota.md

FAIR Dataset documents are avaliable in the fair.md


Config documents are avaliable in the config.md


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


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:

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 Dataset Train Aug Test Aug Optim Lr schd mAP Paper Config Download
S2ANet-R50-FPN DOTA1.0 flip - SGD 1x 74.11 arxiv config model
S2ANet-R50-FPN DOTA1.0 flip+ra90+bc - SGD 1x 76.40 arxiv config model
S2ANet-R50-FPN DOTA1.0 flip+ra90+bc+ms ms SGD 1x 79.72 arxiv config model
S2ANet-R101-FPN DOTA1.0 Flip - SGD 1x 74.28 arxiv config model
Gliding-R50-FPN DOTA1.0 flip+ms ms SGD 1x 67.42 arxiv config model
Gliding-R101-FPN DOTA1.0 flip+ms+ra90+bc ms SGD 1x 69.53 arxiv config model
RetinaNet-R50-FPN DOTA1.0 - - SGD - 62.503 arxiv config model pretrained