# R3Det_Tensorflow **Repository Path**: BlueBuger/R3Det_Tensorflow ## Basic Information - **Project Name**: R3Det_Tensorflow - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 0 - **Created**: 2020-04-13 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object ## Abstract [R3Det](https://arxiv.org/abs/1908.05612) is based on [Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf), and it is completed by [YangXue](https://yangxue0827.github.io/). ## Pipeline ![5](pipeline.png) ## Performance More results and trained models are available in the [MODEL_ZOO.md](MODEL_ZOO.md). ### DOTA1.0 | Model | Backbone | Training data | Val data | mAP | GPU | Image/GPU | Anchor | Reg. Loss| lr schd | Data Augmentation | Configs | |:------------:|:------------:|:------------:|:---------:|:-----------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:| | [RetinaNet](https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation) (baseline) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 62.22 | **1X** GeForce RTX 2080 Ti | 1 | H | smooth L1 | 1x | No | cfgs_res50_dota_v4.py | | [RetinaNet](https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation) (baseline) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 62.79 | 8X GeForce RTX 2080 Ti | 1 | H | smooth L1 | **2x** | No | cfgs_res50_dota_v8.py | | [RetinaNet](https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation) (baseline) | **ResNet101_v1d** 600->800 | DOTA1.0 trainval | DOTA1.0 test | 64.19 | 1X GeForce RTX 2080 Ti | 1 | H | smooth L1 | 1x | No | cfgs_res101_dota_v9.py | | [RetinaNet](https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation) (baseline) | **ResNet152_v1d** 600->800 | DOTA1.0 trainval | DOTA1.0 test | 65.79 | 8X GeForce RTX 2080 Ti | 1 | H | smooth L1 | 2x | No | cfgs_res152_dota_v12.py | | | | | | | | | | | | | | | | [RetinaNet](https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation) (baseline) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 61.94 | 1X GeForce RTX 2080 Ti | 1 | R | smooth L1 | 1x | No | cfgs_res50_dota_v1.py | | [RetinaNet](https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation) (baseline) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 62.25 | **8X** GeForce RTX 2080 Ti | 1 | R | smooth L1 | **2x** | No | cfgs_res50_dota_v10.py | | [RetinaNet](https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 68.65 | 1X GeForce RTX 2080 Ti | 1 | R | [**iou-smooth L1**](https://arxiv.org/abs/1811.07126) | 1x | No | cfgs_res50_dota_v5.py | | | | | | | | | | | | | | | | [R3Det](https://arxiv.org/abs/1908.05612) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 65.73 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 2x | No | cfgs_res50_dota_r3det_v1.py | | [R3Det*](https://arxiv.org/abs/1908.05612) | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 67.20 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 2x | No | cfgs_res50_dota_r3det_v2.py | | [R3Det](https://arxiv.org/abs/1908.05612) | **ResNet101_v1d** 600->800 | DOTA1.0 trainval | DOTA1.0 test | 71.69 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 3x | Yes | - | | [R3Det](https://arxiv.org/abs/1908.05612) | **ResNet152_v1d** 600->800 | DOTA1.0 trainval | DOTA1.0 test | 72.81 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | **4x** | Yes | - | | [R3Det*](https://arxiv.org/abs/1908.05612) | **ResNet152_v1d** 600->800 | DOTA1.0 trainval | DOTA1.0 test | 73.74 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | **4x** | Yes | - | | | | | | | | | | | | | | | | **R3Det++** | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 69.07 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 2x | No | cfgs_res50_dota_r3det_plusplus_v2.py | | R3Det++ | **ResNet152_v1d** 600->800 | DOTA1.0 trainval | DOTA1.0 test | 74.41 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 4x | Yes | - | | R3Det++ | ResNet152_v1d **MS** | DOTA1.0 trainval | DOTA1.0 test | 76.56 | 4X GeForce RTX 2080 Ti | 1 | H + R + more | smooth L1 | 6x | Yes | cfgs_res152_dota_r3det_plusplus_v1.py | [R3Det*](https://arxiv.org/abs/1908.05612): R3Det with two refinement stages **The performance of all models comes from the source [paper](https://arxiv.org/abs/1908.05612).** ### Visualization ![1](demo1.png) ## My Development Environment **docker images: docker pull yangxue2docker/yx-tf-det:tensorflow1.13.1-cuda10-gpu-py3** 1、python3.5 (anaconda recommend) 2、cuda 10.0 3、[opencv(cv2)](https://pypi.org/project/opencv-python/) 4、[tfplot 0.2.0](https://github.com/wookayin/tensorflow-plot) (optional) 5、tensorflow-gpu 1.13 ## Download Model ### Pretrain weights 1、Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz), [resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to data/pretrained_weights. 2、**(Recommend in this repo)** Or you can choose to use a better backbone, refer to [gluon2TF](https://github.com/yangJirui/gluon2TF). * [Baidu Drive](https://pan.baidu.com/s/1GpqKg0dOaaWmwshvv1qWGg), password: 5ht9. * [Google Drive](https://drive.google.com/drive/folders/1BM8ffn1WnsRRb5RcuAcyJAHX8NS2M1Gz?usp=sharing) ## Compile ``` cd $PATH_ROOT/libs/box_utils/cython_utils python setup.py build_ext --inplace (or make) cd $PATH_ROOT/libs/box_utils/ python setup.py build_ext --inplace ``` ## Train 1、If you want to train your own data, please note: ``` (1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py (2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py (3) Add data_name to $PATH_ROOT/data/io/read_tfrecord_multi_gpu.py ``` 2、Make tfrecord For DOTA dataset: ``` cd $PATH_ROOT\data\io\DOTA python data_crop.py ``` ``` cd $PATH_ROOT/data/io/ python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/DOTA/' --xml_dir='labeltxt' --image_dir='images' --save_name='train' --img_format='.png' --dataset='DOTA' ``` 3、Multi-gpu train ``` cd $PATH_ROOT/tools python multi_gpu_train_r3det.py ``` ## Eval ``` cd $PATH_ROOT/tools python test_dota_r3det.py --test_dir='/PATH/TO/IMAGES/' --gpus=0,1,2,3,4,5,6,7 ``` ## Tensorboard ``` cd $PATH_ROOT/output/summary tensorboard --logdir=. ``` ![3](images.png) ![4](scalars.png) ## Citation If this is useful for your research, please consider cite. ``` @article{yang2019r3det, title={R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object}, author={Yang, Xue and Liu, Qingqing and Yan, Junchi and Li, Ang and Zhang, Zhiqiang and Yu, Gang}, journal={arXiv preprint arXiv:1908.05612}, year={2019} } @inproceedings{xia2018dota, title={DOTA: A large-scale dataset for object detection in aerial images}, author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={3974--3983}, year={2018} } ``` ## Reference 1、https://github.com/endernewton/tf-faster-rcnn 2、https://github.com/zengarden/light_head_rcnn 3、https://github.com/tensorflow/models/tree/master/research/object_detection 4、https://github.com/fizyr/keras-retinanet