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. The region proposals can be obtained by setting DumpProposals pseudo metric. The dumped results is a dict(file_name: pred_instance). The pred_instance is an InstanceData containing the sorted boxes and scores predicted by RPN. We provide example of dumping proposals in RPN config.

First, it should be obtained the region proposals in both training and validation (or testing) set. change the type of test_evaluator to DumpProposals in the RPN config to get the region proposals as below:

The config of get training image region proposals can be set as below:

# For training set
val_dataloader = dict(
    dataset=dict(
        ann_file='data/coco/annotations/instances_train2017.json',
        data_prefix=dict(img='val2017/')))
val_dataloader = dict(
    _delete_=True,
    type='DumpProposals',
    output_dir='data/coco/proposals/',
    proposals_file='rpn_r50_fpn_1x_train2017.pkl')
test_dataloader = val_dataloader
test_evaluator = val_dataloader

The config of get validation image region proposals can be set as below:

# For validation set
val_dataloader = dict(
  _delete_=True,
  type='DumpProposals',
  output_dir='data/coco/proposals/',
  proposals_file='rpn_r50_fpn_1x_val2017.pkl')
test_evaluator = val_dataloader

Extract the region proposals command can be set as below:

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

Users can refer to test tutorial for more details.

Then, modify the path of proposal_file in the dataset and using ProposalBroadcaster to process both ground truth bounding boxes and region proposals in pipelines. An example of Fast R-CNN important setting can be seen as below:

train_pipeline = [
    dict(
        type='LoadImageFromFile',
        backend_args={{_base_.backend_args}}),
    dict(type='LoadProposals', num_max_proposals=2000),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='ProposalBroadcaster',
        transforms=[
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
            dict(type='RandomFlip', prob=0.5),
        ]),
    dict(type='PackDetInputs')
]
test_pipeline = [
    dict(
        type='LoadImageFromFile',
        backend_args={{_base_.backend_args}}),
    dict(type='LoadProposals', num_max_proposals=None),
    dict(
        type='ProposalBroadcaster',
        transforms=[
            dict(type='Resize', scale=(1333, 800), keep_ratio=True),
        ]),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor'))
]
train_dataloader = dict(
    dataset=dict(
        proposal_file='proposals/rpn_r50_fpn_1x_train2017.pkl',
        pipeline=train_pipeline))
val_dataloader = dict(
    dataset=dict(
        proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl',
        pipeline=test_pipeline))
test_dataloader = val_dataloader

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}
}