Feature Pyramid Networks for Object Detection

November 30, 2018 · View on GitHub

Recommend an improved version of FPN： https://github.com/DetectionTeamUCAS

A Tensorflow implementation of FPN detection framework.
You can refer to the paper Feature Pyramid Networks for Object Detection
Rotation detection method baesd on FPN reference R2CNN, RRPN and R2CNN_HEAD and R-DFPN
If useful to you, please star to support my work. Thanks.

Configuration Environment

ubuntu(Encoding problems may occur on windows) + python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080
You can also use docker environment, command: docker pull yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0

Installation

Clone the repository

git clone https://github.com/yangxue0827/FPN_Tensorflow.git

Make tfrecord

The data is VOC format, reference here
data path format ($FPN_ROOT/data/io/divide_data.py)

├── VOCdevkit
│   ├── VOCdevkit_train
│       ├── Annotation
│       ├── JPEGImages
│    ├── VOCdevkit_test
│       ├── Annotation
│       ├── JPEGImages

cd $FPN_ROOT/data/io/  
python convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.jpg' --dataset='ship'

Demo

1、Unzip the weight FPNROOT/output/res101trainedweights/∗.rar2、putimagesinFPN_ROOT/output/res101_trained_weights/*.rar 2、put images in FPN_ROOT/tools/inference_image
3、Configure parameters in $FPN_ROOT/libs/configs/cfgs.py and modify the project's root directory 4、image slice

cd $FPN_ROOT/tools
python inference.py

5、big image

cd $FPN_ROOT/tools
python demo.py --src_folder=.\demo_src --des_folder=.\demo_des

Train

1、Modify FPNROOT/libs/lablenamedict/∗∗∗dict.py,correspondingtothenumberofcategoriesintheconfigurationfile2、downloadpretrainweight([resnetv110120160828.tar.gz](http://download.tensorflow.org/models/resnetv110120160828.tar.gz)or[resnetv15020160828.tar.gz](http://download.tensorflow.org/models/resnetv15020160828.tar.gz))from[here](https://github.com/yangxue0827/models/tree/master/slim),thenextracttofolderFPN_ROOT/libs/lable_name_dict/***_dict.py, corresponding to the number of categories in the configuration file 2、download pretrain weight([resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) or [resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)) from [here](https://github.com/yangxue0827/models/tree/master/slim), then extract to folder FPN_ROOT/data/pretrained_weights
3、

cd $FPN_ROOT/tools
python train.py

Test tfrecord

cd $FPN_ROOT/tools    
python $FPN_ROOT/tools/test.py  

cd $FPN_ROOT/tools   
python ship_eval.py

Summary

tensorboard --logdir=$FPN_ROOT/output/res101_summary/

01 02 03

Graph

04

Test results

airplane

11
12

sar_ship

13
14

ship

15
16

Note

This code works better when detecting single targets, but not suitable for multi-target detection tasks. Recommend improved code: https://github.com/DetectionTeamUCAS/FPN_Tensorflow.