JDet

Introduction

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

<!-- **Features** - Automatic compilation. Our framwork is based on Jittor, which means we don't need to Manual compilation for these code with CUDA and C++. - --> <!-- Framework details are avaliable in the [framework.md](docs/framework.md) -->

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. <img src="https://github.com/Jittor/JDet/blob/visualization/docs/images/vis2.jpg?raw=true" alt="Visualization" width="800"/>

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

| Models | Dataset | Sub_Image_Size/Overlap | Train Aug | Test Aug | Optim | Lr schd | mAP | Paper | Config | Download | |:----------------------:|:--------:|:-----------------------:|:---------------:|:---------:|:------:|:--------:|:------:|:---------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------:| | S2ANet-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 74.11 | arxiv | config | model | | S2ANet-R50-FPN | DOTA1.0 | 1024/200 | flip+ra90+bc | - | SGD | 1x | 76.40 | arxiv | config | model | | S2ANet-R50-FPN | DOTA1.0 | 1024/200 | flip+ra90+bc+ms | ms | SGD | 1x | 79.72 | arxiv | config | model | | S2ANet-R101-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 74.28 | arxiv | config | model | | Gliding-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 72.93 | arxiv | config | model | | Gliding-R50-FPN | DOTA1.0 | 1024/200 | Flip+ra90+bc | - | SGD | 1x | 74.93 | arxiv | config | model | | H2RBox-R50-FPN | DOTA1.0 | 1024/200 | flip | - | AdamW | 1x | 67.62 | arxiv | config | model | | RetinaNet-hbb-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 68.02 | arxiv | config | model | | RetinaNet-obb-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 68.07 | arxiv | config | model | | GWD-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 68.88 | arxiv | config | model | | KLD-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 69.10 | arxiv | config | model | |