/Cascade-RCNN_Tensorflow

Primary LanguageJupyter NotebookMIT LicenseMIT

Cascade-RCNN_Tensorflow

Abstract

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

This project is completed by YangXue and WangYashan.

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

1

Comparison

use_voc2012_metric

Stage AP50 AP60 AP70 AP75 AP80 AP85 AP90 AP95
baseline 75.80 67.25 52.15 41.41 27.98 12.63 2.73 0.11
1+2+3 75.80 68.74 57.09 48.68 37.70 22.52 7.51 0.54
1+2 75.98 68.40 56.01 46.89 35.67 20.42 6.44 0.39
1 74.89 65.98 52.45 40.63 27.79 13.22 2.94 0.11
2 75.67 68.69 56.73 47.82 35.5 20.29 6.46 0.38
3 74.35 67.62 56.64 48.65 38.02 23.19 8.05 0.54

use_voc2007_metric

Stage AP50 AP60 AP70 AP75 AP80 AP85 AP90 AP95
baseline 73.62 65.28 51.93 42.52 29.48 16.2 5.84 1.32
1+2+3 73.69 66.59 56.19 48.82 39.47 25.57 12.09 2.5
1+2 74.01 66.5 55.53 46.53 36.96 23.6 11.33 2.15
1 72.92 64.29 52.41 48.8 30.36 16 5.64 2.15
2 73.55 66.75 55.78 48.35 37.39 23.61 10.66 2.69
3 71.58 65.73 56.64 49.08 39.68 26.25 12.28 2.32

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'

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