Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/Jittor/JDet
JDet is an object detection benchmark based on Jittor. Mainly focus on aerial image object detection (oriented object detection).
https://github.com/Jittor/JDet
aerial-image-detection deep-learning jittor object-detection oriented-object-detection
Last synced: 8 days ago
JSON representation
JDet is an object detection benchmark based on Jittor. Mainly focus on aerial image object detection (oriented object detection).
- Host: GitHub
- URL: https://github.com/Jittor/JDet
- Owner: Jittor
- License: apache-2.0
- Created: 2021-08-04T09:13:15.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-08-26T05:30:10.000Z (3 months ago)
- Last Synced: 2024-08-26T06:49:57.636Z (3 months ago)
- Topics: aerial-image-detection, deep-learning, jittor, object-detection, oriented-object-detection
- Language: Python
- Homepage:
- Size: 6.32 MB
- Stars: 185
- Watchers: 11
- Forks: 32
- Open Issues: 33
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# JDet
## Introduction
JDet is an object detection benchmark based on [Jittor](https://github.com/Jittor/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](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-tar))
**Step 1: Install the requirements**
```shell
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](https://github.com/Jittor/jittor)
**Step 2: Install JDet**
```shell
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
```shell
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](docs/dota.md).
FAIR Dataset: [fair.md](docs/fair.md)
SSDD/SSDD+: [ssdd.md](docs/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](docs/config.md) to learn how it works.
### Train
```shell
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.
```shell
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:
```shell
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](docs/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:
```sh
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:
```sh
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](https://arxiv.org/abs/2008.09397) | [config](configs/s2anet/s2anet_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/885e5523d03d42e49984/?dl=1) |
| S2ANet-R50-FPN | DOTA1.0 | 1024/200 | flip+ra90+bc | - | SGD | 1x | 76.40 | [arxiv](https://arxiv.org/abs/2008.09397) | [config](projects/s2anet/configs/s2anet_r50_fpn_1x_dota_rotate_balance.py) | [model](https://cloud.tsinghua.edu.cn/f/41ff7d8ac89d433eb42d/?dl=1) |
| S2ANet-R50-FPN | DOTA1.0 | 1024/200 | flip+ra90+bc+ms | ms | SGD | 1x | 79.72 | [arxiv](https://arxiv.org/abs/2008.09397) | [config](projects/s2anet/configs/s2anet_r50_fpn_1x_dota_rotate_balance_ms.py) | [model](https://cloud.tsinghua.edu.cn/f/9118b7af4497455f8895/?dl=1) |
| S2ANet-R101-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 74.28 | [arxiv](https://arxiv.org/abs/2008.09397) | [config](projects/s2anet/configs/s2anet_r101_fpn_1x_dota_bs2.py) | [model](https://cloud.tsinghua.edu.cn/f/9118b7af4497455f8895/?dl=1) |
| Gliding-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 72.93 | [arxiv](https://arxiv.org/abs/1911.09358) | [config](projects/gliding/configs/gliding_r50_fpn_1x_dota_with_flip.py) | [model](https://cloud.tsinghua.edu.cn/f/d00c40a3ee4e4844a203/?dl=1) |
| Gliding-R50-FPN | DOTA1.0 | 1024/200 | Flip+ra90+bc | - | SGD | 1x | 74.93 | [arxiv](https://arxiv.org/abs/1911.09358) | [config](projects/gliding/configs/gliding_r50_fpn_1x_dota_with_flip_rotate_balance_cate.py) | [model](https://cloud.tsinghua.edu.cn/f/f5a75f9671b04da48498/?dl=1) |
| H2RBox-R50-FPN | DOTA1.0 | 1024/200 | flip | - | AdamW | 1x | 67.62 | [arxiv](https://arxiv.org/abs/2210.06742) | [config](configs/h2rbox/h2rbox_obb_r50_adamw_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/f75f1315c1954f03b9d7/?dl=1) |
| RetinaNet-hbb-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 68.02 | [arxiv](https://arxiv.org/abs/1908.05612) | [config](configs/rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/f511f182a6124c42bceb/?dl=1) |
| RetinaNet-obb-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 68.07 | [arxiv](https://arxiv.org/abs/1908.05612) | [config](configs/rotated_retinanet/rotated_retinanet_obb_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/104debafe3b8456297ca/?dl=1) |
| GWD-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 68.88 | [arxiv](https://arxiv.org/abs/2101.11952) | [config](projects/rotated_retinanet_gwd/configs/rotated_retinanet_hbb_gwd_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/e7c6c120a0c24713a568/?dl=1) |
| KLD-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 69.10 | [arxiv](https://arxiv.org/abs/2106.01883) | [config](projects/rotated_retinanet_kld/configs/rotated_retinanet_hbb_kld_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/cbe389f359b743a0ac4f/?dl=1) |
| KFIoU-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 69.36 | [arxiv](https://arxiv.org/abs/2201.12558)| [config](projects/rotated_retinanet_kfiou/configs/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/cbe389f359b743a0ac4f/?dl=1) |
| FasterRCNN-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 69.631 | [arxiv](https://arxiv.org/abs/1506.01497) | [config](configs/faster_rcnn_obb_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/d73fb4bfc01140f1a331/?dl=1) |
| RoITransformer-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 73.842 | [arxiv](https://arxiv.org/abs/1812.00155) | [config](configs/faster_rcnn_RoITrans_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/2bc5c045874349859b32/?dl=1) |
| FCOS-R50-FPN | DOTA1.0 | 1024/200 | flip | - | SGD | 1x | 70.40 | [ICCV19](https://openaccess.thecvf.com/content_ICCV_2019/papers/Tian_FCOS_Fully_Convolutional_One-Stage_Object_Detection_ICCV_2019_paper.pdf) | [config](configs/fcos_obb_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/5df0364e558043e18b0c/?dl=1) |
| OrientedRCNN-R50-FPN | DOTA1.0 | 1024/200 | Flip | - | SGD | 1x | 75.62 | [ICCV21](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf) | [config](configs/oriented_rcnn_r50_fpn_1x_dota_with_flip.py) | [model](https://cloud.tsinghua.edu.cn/f/6282cfab78804280abf8/?dl=1) |
| ReDet-R50-FPN |DOTA1.0|1024/200|Flip|-| SGD | 1x | 76.23 | [arxiv](https://arxiv.org/abs/2103.07733)| [config](configs/ReDet_re50_refpn_1x_dota1.py) | [model](https://cloud.tsinghua.edu.cn/f/d49722c4480d46f0930d/?dl=1) [pretrained](https://cloud.tsinghua.edu.cn/f/cd013121de4e45189ebf/?dl=1) |
| CSL-R50-FPN |DOTA1.0|1024/200| flip|-| SGD | 1x | 67.99 | [arxiv](https://link.springer.com/chapter/10.1007/978-3-030-58598-3_40)| [config](configs/rotated_retinanet_obb_csl_gaussian_r50_fpn_1x_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/8a192907e94d4fd08e2d/?dl=1) |
| RSDet-R50-FPN | DOTA1.0|1024/200|Flip|-| SGD | 1x | 68.41 | [arxiv](https://arxiv.org/abs/1911.08299) | [config](configs/rotated_retinanet/rsdet_obb_r50_fpn_1x_dota_lmr5p.py) | [model](https://cloud.tsinghua.edu.cn/f/fb98782e976f4fa69045/?dl=1) |
| ATSS-R50-FPN|DOTA1.0|1024/200| flip|-| SGD | 1x | 72.44 | [arxiv](https://arxiv.org/abs/1912.02424) | [config](configs/rotated_retinanet/rotated_retinanet_obb_r50_fpn_1x_dota_atss.py) | [model](https://cloud.tsinghua.edu.cn/f/b9e9dc675e9742a28383/?dl=1) |
| Reppoints-R50-FPN|DOTA1.0|1024/200| flip|-| SGD | 1x | 56.34 | [arxiv](https://arxiv.org/abs/1904.11490) | [config](configs/rotated_retinanet/rotated_retinanet_obb_r50_fpn_1x_dota_atss.py) | [model](https://cloud.tsinghua.edu.cn/f/af7dcbb1a16e450c8182/?dl=1) |
**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
7. 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
- :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: [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](https://cg.cs.tsinghua.edu.cn/). 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](https://github.com/Jittor/jittor)
2. [mmrotate](https://github.com/open-mmlab/mmrotate)
3. [Detectron2](https://github.com/facebookresearch/detectron2)
4. [mmdetection](https://github.com/open-mmlab/mmdetection)
5. [maskrcnn_benchmark](https://github.com/facebookresearch/maskrcnn-benchmark)
6. [RotationDetection](https://github.com/yangxue0827/RotationDetection)
7. [s2anet](https://github.com/csuhan/s2anet)
8. [gliding_vertex](https://github.com/MingtaoFu/gliding_vertex)
9. [oriented_rcnn](https://github.com/jbwang1997/OBBDetection/tree/master/configs/obb/oriented_rcnn)
10. [r3det](https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection)
11. [AerialDetection](https://github.com/dingjiansw101/AerialDetection)
12. [DOTA_devkit](https://github.com/CAPTAIN-WHU/DOTA_devkit)
13. [OBBDetection](https://github.com/jbwang1997/OBBDetection)