The code is being sorted out and the complete code and instructions are will uploaded soon.Stay tuned.

A Tensorflow implementation of FPN or R2CNN detection framework based on FPN . The paper references R2CNN Rotational Region CNN for Orientation Robust Scene Text Detection or Feature Pyramid Networks for Object Detection

Configuration Environment

ubuntu + python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080
If you want to use cpu, you need to modify the parameters of NMS and IOU functions use_gpu = False
You can also use docker environment, command: docker push yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0

Make tfrecord

data path format
VOCdevkit

VOCdevkit_train

Annotation
JPEGImages

VOCdevkit_test

Annotation
JPEGImages

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

Train

1、Configure parameters in ./libs/configs/cfgs.py and modify the project's root directory
2、Modify ./libs/lable_name_dict.py, corresponding to the number of categories in the configuration file
3、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 ./data/pretrained_weights 4、Choose a model(FPN and R2CNN)
If you want to train FPN:

python ./tools/train.py

elif you want to train R2CNN:

python ./tools/train1.py

Test tfrecord

mkdir test_result
python ./tools/test.py(test1.py)

Test images

put images in ./tools/inference_image, and mkdir inference_result
python ./tools/inference.py(inference1.py)

eval

python ./tools/eval.py(eval1.py)

Summary

tensorboard ---logdir=./output/summary/

Test results