This repo is based on Focal Loss for Dense Object Detection, and it is completed by YangXue.
This is the baseline work of R3Det, paper link: R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object.
| Model | Backbone | Training data | Val data | mAP | GPU | Image/GPU | Anchor | Reg. Loss | lr schd | Data Augmentation | configs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| RetinaNet (baseline) | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 53.17 | 8X GeForce RTX 2080 Ti | 1 | H | smooth L1 | 1x | No | cfgs_res50_dota_v3.py |
| RetinaNet (baseline) | ResNet50_v1 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 (baseline) | ResNet50_v1 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 (baseline) | ResNet50_v1 800->1024 | DOTA1.0 trainval | DOTA1.0 test | 60.32 | 8X GeForce RTX 2080 Ti | 1 | H | smooth L1 | 2x | No | cfgs_res50_dota_v14.py |
| RetinaNet (baseline) | ResNet101_v1 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 (baseline) | ResNet152_v1 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 (baseline) | ResNet50_v1 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 (baseline) | ResNet50_v1 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 | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 62.69 | 1X GeForce RTX 2080 Ti | 1 | R | iou-smooth L1 | 1x | No | cfgs_res50_dota_v5.py |
| R3Det | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 65.73 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 2x | No | - |
| R3Det | ResNet50_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 67.55 | 8X GeForce RTX 2080 Ti | 1 | H + R + more anchor | smooth L1 | 2x | No | - |
| R3Det | ResNet101_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 71.69 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 3x | Yes | - |
| R3Det | ResNet152_v1 600->800 | DOTA1.0 trainval | DOTA1.0 test | 72.81 | 8X GeForce RTX 2080 Ti | 1 | H + R | smooth L1 | 4x | Yes | - |
1、python3.5 (anaconda recommend)
2、cuda 9.0
3、opencv(cv2)
4、tfplot 0.2.0 (optional)
5、tensorflow 1.12
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects (ICCV2019)
1、Please download resnet50_v1, resnet101_v1 pre-trained models on Imagenet, put it to data/pretrained_weights.
2、Or you can choose to use a better backbone, refer to gluon2TF. Pretrain Model Link, password: 5ht9. (Recommend)
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
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.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.py
cd $PATH_ROOT/tools
python test_dota.py --test_dir='/PATH/TO/IMAGES/'
--gpus=0,1,2,3,4,5,6,7
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
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