JDet
March 2, 2025 ยท View on GitHub
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
| 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 |
| KFIoU-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 69.36 | arxiv | config | model |
| FasterRCNN-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 69.631 | arxiv | config | model |
| RoITransformer-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 73.842 | arxiv | config | model |
| FCOS-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 70.40 | ICCV19 | config | model |
| OrientedRCNN-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 75.62 | ICCV21 | config | model |
| ReDet-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 76.23 | arxiv | config | model pretrained |
| CSL-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 67.99 | arxiv | config | model |
| RSDet-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 68.41 | arxiv | config | model |
| ATSS-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 72.44 | arxiv | config | model |
| Reppoints-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 56.34 | arxiv | config | model |
| OrientedRCNN-LSKNet-S-FPN | DOTA1.0 | 1024/200 | flip+ra90 | - | AdamW | 1x | 77.17 | IJCV | config | pretrained |
| StripRCNN-StripNet-S-FPN | DOTA1.0 | 1024/200 | flip+ra90 | - | AdamW | 1x | 77.36 | arxiv | config | pretrained |
Notice:
- ms: multiscale
- flip: random flip
- ra: rotate aug
- ra90: rotate aug with angle 90,180,270
- 1x : 12 epochs
- bc: balance category
- 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
- :heavy_check_mark: LSKNet
- :heavy_check_mark: StripRCNN
- :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: Sampling Equivariant Self-attention Networks
- :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: 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}
}