# JDet **Repository Path**: Jittor/JDet ## Basic Information - **Project Name**: JDet - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 0 - **Created**: 2021-08-05 - **Last Updated**: 2023-10-21 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## 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). Visualization ### 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/d/918bcbf7a10a40fb8dee/files/?p=%2Fmodels%2Fs2anet_r50_fpn_1x_dota_bs2_steplr_3%2Fckpt_12.pkl&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/d/918bcbf7a10a40fb8dee/files/?p=%2Fmodels%2Fs2anet_r50_fpn_1x_dota_rotate_balance%2Fckpt_12.pkl&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/d/918bcbf7a10a40fb8dee/files/?p=%2Fmodels%2Fs2anet_r50_fpn_1x_dota_rotate_balance_ms%2Fckpt_12.pkl&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/d/918bcbf7a10a40fb8dee/files/?p=%2Fmodels%2Fs2anet_r101_fpn_1x_dota_without_torch_pretrained%2Fckpt_12.pkl&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/ebeefa1edaf84a4d8a2a/?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/395ecd3ddaf44bb58ac9/?dl=1) | | RetinaNet-R50-FPN |DOTA1.0|600/150|-|-| SGD | - | 62.503 | [arxiv](https://arxiv.org/abs/1708.02002)| [config](configs/retinanet_r50v1d_fpn_dota.py) | [model](https://cloud.tsinghua.edu.cn/f/f12bb566d4be43bfbdc7/) [pretrained](https://cloud.tsinghua.edu.cn/f/6b5db5fdd5304a5abf19/) | | 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/29197095057348d0a392/?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/55fe6380928f4a6582f8/?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/d/918bcbf7a10a40fb8dee/files/?p=%2Fmodels%2Ffcos_r50%2Fckpt_12.pkl&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/a50517f7b8e840949d3f/?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: 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 - :clock3: ReDet - :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: Double Head OBB - :heavy_plus_sign: Oriented Reppoints - :heavy_plus_sign: Guided Anchoring - :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](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. [Detectron2](https://github.com/facebookresearch/detectron2) 3. [mmdetection](https://github.com/open-mmlab/mmdetection) 4. [maskrcnn_benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) 5. [RotationDetection](https://github.com/yangxue0827/RotationDetection) 6. [s2anet](https://github.com/csuhan/s2anet) 7. [gliding_vertex](https://github.com/MingtaoFu/gliding_vertex) 8. [oriented_rcnn](https://github.com/jbwang1997/OBBDetection/tree/master/configs/obb/oriented_rcnn) 9. [r3det](https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection) 10. [AerialDetection](https://github.com/dingjiansw101/AerialDetection) 11. [DOTA_devkit](https://github.com/CAPTAIN-WHU/DOTA_devkit) 12. [OBBDetection](https://github.com/jbwang1997/OBBDetection)