configs/backup/fast_rcnn/README.md

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}
}