This is a tensorflow re-implementation of R-FCN: Object Detection via Region-based Fully Convolutional Networks.
This project is completed by YangXue and YangJirui.
| Models | mAP | sheep | horse | bicycle | bottle | cow | sofa | bus | dog | cat | person | train | diningtable | aeroplane | car | pottedplant | tvmonitor | chair | bird | boat | motorbike |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| resnet101_v1 | 72.33 | 73.32 | 80.55 | 81.45 | 52.44 | 77.50 | 74.45 | 82.45 | 83.77 | 85.15 | 79.82 | 82.05 | 63.21 | 77.12 | 84.42 | 38.43 | 71.72 | 49.44 | 72.86 | 56.56 | 80.09 |
1、tensorflow >= 1.2
2、cuda8.0
3、python2.7 (anaconda2 recommend)
4、opencv(cv2)
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.
├── VOCdevkit
│ ├── VOCdevkit_train
│ ├── Annotation
│ ├── JPEGImages
│ ├── VOCdevkit_test
│ ├── Annotation
│ ├── JPEGImages
cd $PATH_ROOT/libs/box_utils/cython_utils
python setup.py build_ext --inplace
cd $PATH_ROOT/tools
python eval.py --eval_imgs='/PATH/TO/IMAGES/'
--annotation_dir='/PATH/TO/TEST/ANNOTATION/'
--GPU='0'
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
cd $PATH_ROOT/output/summary
tensorboard --logdir=.
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