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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.)

1

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_v1resnet101_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(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=.

2 1

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


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