RRPN_pytorch

RRPN in pytorch, which is implemented into facebook's benchmark: https://github.com/facebookresearch/maskrcnn-benchmark. Its caffe version can be viewed at: https://github.com/mjq11302010044/RRPN.

Highlights

From original repo: In pytorch 1.0, Somehow faster than original repo in both training and inference.
Training and evaluation checked: Testing in IC15 with training data in {IC13, IC15, IC17mlt}, and receives Fscore of 83% vs. 81% in caffe repo.
What's new: RRoI Pooling is replaced with RRoI Align(bilinear interpolation for sampling), FPN structure supported, easy to change various backbones for different purposes.

Installation

Check INSTALL.md for installation instructions.

Configuring your dataset

Your dataset path can be set in $RRPN_ROOT/maskrcnn_benchmark/config/paths_catalog.py. We implemented interface for {IC13, IC15, IC17mlt, LSVT, ArT} for common use(Start from line 96):

...
 "RRPN_train": {  # including IC13 and IC15
            'dataset_list':{
                # 'IC13': 'Your dataset path',
                ...
            },
            "split": 'train'
        },
...

Add your dataset? You need to form a dict array as follows:

im_info = {
    'gt_classes': your class_id array,
    'max_classes': your class_id array,
    'image': path to access one image,
    'boxes': rotate box in {cx, cy, w, h, θ},
    'flipped': Not supported, just False, 
    'gt_overlaps': overlaps fill with 1 (gt with gt),
    'seg_areas': H * W for an rbox,
    'height': height of an image,
    'width': width of an image,
    'max_overlaps': overlaps fill with 1 (gt with gt),
    'rotated': just True
}

Examples can be seen in $RRPN_ROOT/maskrcnn_benchmark/data/rotation_series.py Your data API should be add to the variable DATASET:

DATASET = {
    'IC13':get_ICDAR2013,
    'IC15':get_ICDAR2015_RRC_PICK_TRAIN,
    'IC17mlt':get_ICDAR2017_mlt,
    ...
    'Your Dataset Name': 'Your Dataset API'
}

Training

# In your root of RRPN
python tools/train_net.py --config-file=configs/rrpn/e2e_rrpn_R_50_C4_1x_ICDAR13_15_17_trial.yaml

Multi-GPU phase is not testing yet, be careful to use GPU more than 1.

Testing

Using $RRPN_ROOT/demo/RRPN_Demo.py to test images you want. The demo will generate a text for your detected coodinates.
Showing the detected image by ture the variable vis to True.

Final

Enjoy it with all the codes.
Citing us if you find it work in your projects.

@misc{ma2019rrpn,
    author = {Jianqi Ma},
    title = {{RRPN in pytorch}},
    year = {2019},
    howpublished = {\url{https://github.com/mjq11302010044/RRPN_pytorch}},
}
@article{Jianqi17RRPN,
    Author = {Jianqi Ma and Weiyuan Shao and Hao Ye and Li Wang and Hong Wang and Yingbin Zheng and Xiangyang Xue},
    Title = {Arbitrary-Oriented Scene Text Detection via Rotation Proposals},
    journal = {IEEE Transactions on Multimedia},
    volume={20}, 
    number={11}, 
    pages={3111-3122}, 
    year={2018}
}