TRDet

Abstract

This project is based on R²CNN, and completed by BaochaiPeng .

Requirements

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

Download Model

1、please download resnet50_v1、resnet101_v1 pre-trained models on Imagenet, put it to data/pretrained_weights.
2、please download mobilenet_v2 pre-trained model on Imagenet, put it to data/pretrained_weights/mobilenet.
3、please download trained model by this project, put it to output/trained_weights.

Data Prepare

1、crop data, reference:

cd $PATH_ROOT/data/io/data
python train_crop.py 
python val_crop.py

2、data format

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

Compile

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

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

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 $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='.png' 
                                   --dataset='dataset'

3、train

cd $PATH_ROOT/tools
python train.py

Eval

python eval.py --img_dir='/PATH/TO/dataset/IMAGES/' 
               --image_ext='.png' 
               --test_annotation_path='/PATH/TO/TEST/ANNOTATION/'
               --gpu='0'

Tensorboard

cd $PATH_ROOT/output/summary
tensorboard --logdir=.