Fast R-CNN

Fast R-CNN

Abstract

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate.

Introduction

Before training the Fast R-CNN, users should first train an RPN, and use the RPN to extract the region proposals.

Firstly, extract the region proposals of the val set by this command as below:

./tools/dist_test.sh \
    configs/rpn_r50_fpn_1x_coco.py \
    checkpoints/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth \
    8 \
    --out proposals/rpn_r50_fpn_1x_val2017.pkl

Then, change the ann_file and img_prefix of data.test in the RPN config to train set as below:

data = dict(
    test=dict(
        ann_file='data/coco/annotations/instances_train2017.json',
        img_prefix='data/coco/train2017/'))

Extract the region proposals of the train set by this command as below:

./tools/dist_test.sh \
    configs/rpn_r50_fpn_1x_coco.py \
    checkpoints/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth \
    8 \
    --out proposals/rpn_r50_fpn_1x_train2017.pkl

Modify the path of proposal_file in Fast R-CNN config as below:

data = dict(
    train=dict(
        proposal_file='proposals/rpn_r50_fpn_1x_train2017.pkl'),
    val=dict(
        proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl'),
    test=dict(
        proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl'))

Finally, users can start training the Fast R-CNN.

Results and Models

Citation

@inproceedings{girshick2015fast,
  title={Fast r-cnn},
  author={Girshick, Ross},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  year={2015}
}