DetectionTeamUCAS / Retinanet_tensorflow_rotation
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Focal Loss for Dense Rotation Object Detection
Abstract
This repo is based on Focal Loss for Dense Object Detection, and it is completed by YangXue.
We also recommend a tensorflow-based rotation detection benchmark, which is led by YangXue.
Performance
DOTA1.0
| Model | Backbone | Training data | Val data | mAP | Model Link | Anchor | Reg. Loss | Angle Range | lr schd | Data Augmentation | GPU | Image/GPU | Configs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RetinaNet | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 63.18 | model | H | smooth L1 | 90 | 1x | No | 1X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v4.py |
| RetinaNet | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 64.10 | - | H | smooth L1 | 180 | 1x | No | 1X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v15.py |
| RetinaNet | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 62.76 | model | R | smooth L1 | 90 | 1x | No | 1X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v1.py |
| RetinaNet | ResNet50_v1d 600->800 | DOTA1.0 trainval | DOTA1.0 test | 68.65 | - | R | iou-smooth L1 | 90 | 1x | No | 1X GeForce RTX 2080 Ti | 1 | cfgs_res50_dota_v5.py |
Notice:
Due to the improvement of the code, the performance of this repo is gradually improving, so the experimental results in configuration files are for reference only.
This repo has basically stopped updating, please refer to new repo for the latest progress.
Visualization
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)
4、tfplot 0.2.0 (optional)
5、tensorflow 1.13
IoU-smooth L1 Loss
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects (ICCV2019)
Download Model
Pretrain weights
1、Please download resnet50_v1, resnet101_v1 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.
- Baidu Drive, password: 5ht9.
- Google Drive
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/label_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
Test
cd $PATH_ROOT/tools
python test_dota.py --test_dir='/PATH/TO/IMAGES/'
--gpus=0,1,2,3,4,5,6,7
Notice: In order to set the breakpoint conveniently, the read and write mode of the file is' a+'. If the model of the same #VERSION needs to be tested again, the original test results need to be deleted.
Tensorboard
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
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