Faster-RCNN_Tensorflow

Abstract

This is a tensorflow re-implementation of Faster R-CNN: Towards Real-Time ObjectDetection with Region Proposal Networks.

This project is completed by YangXue and YangJirui. Some relevant projects (R²CNN) and (RRPN) based on this code.

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

Comparison

use_voc2012_metric

Models	mAP	sheep	horse	bicycle	bottle	cow	sofa	bus	dog	cat	person	train	diningtable	aeroplane	car	pottedplant	tvmonitor	chair	bird	boat	motorbike
resnet50_v1	75.16	74.08	89.27	80.27	55.74	83.38	69.35	85.13	88.80	91.42	81.17	81.71	62.74	78.65	86.86	47.00	76.71	50.29	79.05	60.51	80.96
resnet101_v1	77.03	79.68	89.33	83.89	59.41	85.68	76.59	84.23	88.50	88.50	81.54	79.16	72.66	80.26	88.42	47.50	79.81	52.85	80.70	59.94	81.87
mobilenet_v2	50.36	46.68	70.45	67.43	25.69	53.60	46.26	58.95	37.62	43.97	67.67	61.35	52.14	56.54	75.02	24.47	49.89	27.76	38.04	38.20	65.46

use_voc2007_metric

Models	mAP	sheep	horse	bicycle	bottle	cow	sofa	bus	dog	cat	person	train	diningtable	aeroplane	car	pottedplant	tvmonitor	chair	bird	boat	motorbike
resnet50_v1	73.09	72.11	85.63	77.74	55.82	81.19	67.34	82.44	85.66	87.34	77.49	79.13	62.65	76.54	84.01	47.90	74.13	50.09	76.81	60.34	77.47
resnet101_v1	74.63	76.35	86.18	79.87	58.73	83.4	74.75	80.03	85.4	86.55	78.24	76.07	70.89	78.52	86.26	47.80	76.34	52.14	78.06	58.90	78.04
mobilenet_v2	50.34	46.99	68.45	65.89	28.16	53.21	46.96	57.80	38.60	44.12	66.20	60.49	52.40	56.06	72.68	26.91	49.99	30.18	39.38	38.54	64.74

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 $PATH_ROOT/data/pretrained_weights. 2、please download [mobilenet_v2](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) pre-trained model on Imagenet, put it to$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(available)

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'

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/lable_dict.py     
(3) Add data_name to line 76 of $PATH_ROOT/data/io/read_tfrecord.py 

2、make tfrecord

cd $PATH_ROOT/data/io/  
python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/VOCdevkit/VOCdevkit_train/' 
                                   --xml_dir='Annotation'
                                   --image_dir='JPEGImages'
                                   --save_name='train' 
                                   --img_format='.jpg' 
                                   --dataset='pascal'

3、train

cd $PATH_ROOT/tools
python train.py

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