wangguojun2018/Soft-NMS_Cascade-RCNN

A Tensorflow implementation of Soft-NMS Cascade R-CNN: Delving into High Quality Object Detection.

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Soft-NMS_Cascade-RCNN

Abstract

This is a tensorflow re-implementation of Cascade R-CNN Delving into High Quality Object Detection .

This project is based on Cascade-RCNN_Tensorflow by YangXue and WangYashan. This project fixed the bug about MobilenetV2 and add Vgg16 backbone. Besides Soft-Nms are implemented

Train on VOC 2007 trainval and test on VOC 2007 test (PS. This project also support coco training.)

Comparison

use_voc2007_metric

method	AP	AP50	AP60	AP70	AP80	AP90
Vgg16 Faster-RCNN	0.4059	0.6954	0.6032	0.4506	0.2319	0.0488
Soft-NMS Vgg16 Faster-RCNN		0.6946
Vgg16 Cascade-RCNN	0.4468	0.6856	0.6120	0.4951	0.3331	0.1080
MobilenetV2-Faster-RCNN		0.5416
Soft-NMS MobilenetV2-Faster-RCNN		0.5423

Requirements

1、tensorflow >= 1.2
2、cuda8.0
3、python2.7 (anaconda2 recommend)
4、opencv(cv2)

Download Model

1、please download resnet50_v1、resnet101_v1 、

pre-trained models on Imagenet, put it to $PATH_ROOT/data/pretrained_weights.
2、please download mobilenet_v2 pre-trained model on Imagenet, put it to $PATH_ROOT/data/pretrained_weights/mobilenet.
3、please download trained model by this project, put it to $PATH_ROOT/output/trained_weights.

Data Format

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

Compile

cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace

Demo

Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py, then download the corresponding weights.

cd $PATH_ROOT/tools
python inference.py --data_dir='/PATH/TO/IMAGES/' 
                    --save_dir='/PATH/TO/SAVE/RESULTS/' 
                    --GPU='0'

Eval

cd $PATH_ROOT/tools
python eval.py --eval_imgs='/PATH/TO/IMAGES/'  
               --annotation_dir='/PATH/TO/TEST/ANNOTATION/'
               --GPU='0'

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/DetectionTeamUCAS/Cascade-RCNN_Tensorflow.git