JDet¶
Github: https://github.com/Jittor/JDet
Introduction¶
JDet is a object detection benchmark 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/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
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¶
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 |
config |
||
S2ANet-R50-FPN |
DOTA1.0 |
1024/200 |
flip+ra90+bc |
- |
SGD |
1x |
76.40 |
config |
||
S2ANet-R50-FPN |
DOTA1.0 |
1024/200 |
flip+ra90+bc+ms |
ms |
SGD |
1x |
79.72 |
config |
||
S2ANet-R101-FPN |
DOTA1.0 |
1024/200 |
Flip |
- |
SGD |
1x |
74.28 |
config |
||
Gliding-R50-FPN |
DOTA1.0 |
1024/200 |
flip+ms |
ms |
SGD |
1x |
67.42 |
arxiv |
config |
|
Gliding-R101-FPN |
DOTA1.0 |
1024/200 |
flip+ms+ra90+bc |
ms |
SGD |
1x |
69.53 |
arxiv |
config |
|
RetinaNet-R50-FPN |
DOTA1.0 |
600/150 |
- |
- |
SGD |
- |
62.503 |
config |
Notice:
ms: multiscale
flip: random flip
ra: rotate aug
ra90: rotate aug with angle 90,180,270
1x : 12 epochs
bc: balance category
Plan¶
: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: Faster R-CNN
:heavy_check_mark: SSD
:heavy_check_mark: ROI Transformer
:clock3: ReDet
:clock3: YOLOv5
:clock3: R3Det
:clock3: Cascade R-CNN
:heavy_plus_sign: CSL
:heavy_plus_sign: DCL
:heavy_plus_sign: GWD
:heavy_plus_sign: KLD
:heavy_plus_sign: …
Contact Us¶
Website: http://cg.cs.tsinghua.edu.cn/jittor/
Email: jittor@qq.com
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}
}