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README.md | 9 meses atrás | |
fast-rcnn_r101-caffe_fpn_1x_coco.py | 9 meses atrás | |
fast-rcnn_r101_fpn_1x_coco.py | 9 meses atrás | |
fast-rcnn_r101_fpn_2x_coco.py | 9 meses atrás | |
fast-rcnn_r50-caffe_fpn_1x_coco.py | 9 meses atrás | |
fast-rcnn_r50_fpn_1x_coco.py | 9 meses atrás | |
fast-rcnn_r50_fpn_2x_coco.py | 9 meses atrás |
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.
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.
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.
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
@inproceedings{girshick2015fast,
title={Fast r-cnn},
author={Girshick, Ross},
booktitle={Proceedings of the IEEE international conference on computer vision},
year={2015}
}