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.
ubuntu(Encoding problems may occur on windows) + python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080
You can also use docker environment, command: docker push yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0
Clone the repository
git clone https://github.com/yangxue0827/FPN_Tensorflow.git The image name is best in English.
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'1、Unzip the weight $FPN_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_ROO
7E96
T/tools
python inference.py 5、big image
cd $FPN_ROOT/tools
python demo.py --src_folder=.\demo_src --des_folder=.\demo_des 1、Modify $FPN_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 or resnet_v1_50_2016_08_28.tar.gz) from here, then extract to folder $FPN_ROOT/data/pretrained_weights
3、
cd $FPN_ROOT/tools
python train.py cd $FPN_ROOT/tools
python $FPN_ROOT/tools/test.py cd $FPN_ROOT/tools
python ship_eval.pytensorboard --logdir=$FPN_ROOT/output/res101_summary/
This code works better when detecting single targets, but not suitable for multi-target detection tasks. Hope you can help find bugs, thank you very much.