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# Fast R-CNN
> [Fast R-CNN](https://arxiv.org/abs/1504.08083)
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## 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.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143882189-6258c05c-f2a1-4320-9282-7e2f2d502eb2.png"/>
</div>
## Introduction
Before training the Fast R-CNN, users should first train an [RPN](../rpn/README.md), 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](../rpn/rpn_r50_fpn_1x_coco.py).
- 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:
```python
# 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:
```python
# 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:
```bash
./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](https://mmdetection.readthedocs.io/en/latest/user_guides/test.html) 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:
```python
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
```latex
@inproceedings{girshick2015fast,
title={Fast r-cnn},
author={Girshick, Ross},
booktitle={Proceedings of the IEEE international conference on computer vision},
year={2015}
}
```
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