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Fast R-CNN

总结

  • 本文是结合RCNN和SPPnet的改进版本
    • 大幅提成训练/测试速度
    • 实现端对端一体化训练

对前作缺点分析

  • RCNN
    • 多阶段训练
    • 训练慢,也占用硬盘空间
    • inference也慢
  • SPPnet
    • 多阶段训练
    • fine-tuning阶段无法更新conv网络参数

网络结构

  • 借用SPPnet的思想,每次训练只有一次整体图片的卷积计算,然后通过映射截取特定proposal的卷积值
  • 然后过RoI pooling层(窗口大小和步长根据输入大小动态计算)得到定长特征向量
  • 舍弃SVM训练阶段,直接使用网络softmax去做分类
  • 特征向量同时给到bounding-box回归训练流程,结合两个部分组成一个新的损失函数一起训练

训练样本组成

  • 首先分析了SPPnet不能fine-tuning卷积层参数的原因——不同图片的proposal需要重新计算卷积值走了RCNN的老路,耗时太长
  • 所以在sampling阶段,尽量用同一图片的proposal构成batch
  • 实验证明,选取同一图片的proposal没有收敛之类的问题

FC层分解

  • 简化了卷积层的计算步骤之后,FC层的计算就显得时间过长了(总计算时长的38.7%)
  • 利用因式分解的思想简化计算——将uv的计算量分解为t (u + v)其中$t \ll min(u, v)$

其他的思考

  • fine-tuning全部网络是否有用?——用处不大,且会让训练速度大大降低,所以一般只fine-tuning后面几层(具体哪几层根据网络深度而定)
  • 分多任务训练是否有帮助?——把分类损失和bounding-box回归损失结合在一起的效果最好,原因可能是分类任务&bounding任务共同反馈使网络学习的更好
  • 尺度不变性用多尺度抽取还是单尺度?——多尺度效果更好但有限;在大型网络中因为GPU内存的限制(暂时)用不了多尺度,此时单尺度的效果超越中、小网络的多尺度,所以网络结构才是关键。选择尽量深的网络加单尺度
  • 需要更多的训练数据?——是的,训练数据越多越好
  • SVMs比softmax表现效果更好?——对,感觉有点打脸?RCNN里一通分析为啥要单拎出来一个训练流程做分类,结果这次直接合在一块而且效果更好了。除了文章中提到的softmax训练引入了不同类别之间的竞争,个人认为还有一个原因是合并流程把bounding-box回归也合并进来了共同提升了效果
  • 更多的proposal会提升效果?——不是,实验证明后续proposal变多会让mAP变低

VOC测试结果

  • time: 1830ms
  • VOC 2007
    • 07 data: 66.9%
    • 07+12 data: 70%
  • VOC 2010
    • 12 data: 66.1%
    • 07+12 data: 68.8%
  • VOC 2012
    • 12 data: 65.7%
    • 07+12 data: 68.4%

Abstrct

Fast R-CNN trains the very deep VGG16 network 9× faster than R-CNN

Introduction

two primary challenges:

  • numerous candidate object locations (often called “proposals”) must be processed
  • these candidates provide only rough localization that must be refined to achieve precise localization

We propose a single-stage training algorithm that jointly learns to classify object proposals and refine their spatial locations.

R-CNN and SPPnet

R-CNN’s drawbacks:

  • training is a multi-stage pipeline
  • training is expensive in space and time
  • object detection is slow

R-CNN is slow because it performs a ConvNet forward pass for each object proposal

SPPnet just compute convolutional feature map once per image

SPPnet’s drawbacks:

  • training is a multi-stage pipeline
  • fine-tuning algorithm cannot update the convolutional layers

contributions

  • higher detection quality
  • training is single-stage, using a multi-task loss
  • training can update all network layers
  • no disk storage is required for feature caching

Fast R-CNN Architecture and Training

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  • the network first processes the whole image with several convolutional (conv) and max pooling layers to produce a conv feature map
  • for each object proposal a region of interest (RoI) pooling layer extracts a fixed-length feature vector from the feature map. Each feature vector is fed into a sequence of fully connected
    • one that produces softmax probability estimates over K object classes plus a catch-all “background” class
    • outputs four real-valued numbers for each of the K object classes

the RoI pooling layer

each RoI is defined by a four-tuple (r, c, h, w) that specifies its top-left corner (r, c) and its height and width (h, w)

the RoI layer is simply the special-case of the spatial pyramid pooling layer used in SPPnets 11 in which there is only one pyramid level.

initializing from pre-trained networks

three transformations:

  • the last max pooling layer is replaced by a RoI pooling layer
  • the network’s last fully connected layer and softmax are replaced with the two sibling layers
  • the network is modified to take two data inputs: a list of images and a list of RoIs in those images

fine-tuning for detection

Question:

  • why SPPnet is unable to update weights below the spatial pyramid pooling layer

Answer:

  • that back-propagation through the SPP layer is highly inefficient when each training sample (i.e. RoI) comes from a different image
  • the inefficiency stems from the fact that each RoI may have a very large receptive field, often spanning the entire input image
  • the training inputs are large

Solution:

  • sampled hierarchically
  • first by sampling N images and then by sampling R/N RoIs from each image
  • RoIs from the same image share computation and memory in the forward and backward passes
  • don’t cause slow training convergence in practice
  • N = 2 and R = 128

multi-task loss

We use a multi-task loss L on each labeled RoI to jointly train for classification and bounding-box regression

  • in which $L_{cls} (p, u) = - logP_u$
  • For background RoIs there is no notion of a ground-truth bounding box and hence $L_{loc}$ is ignored
  • $L_{loc}(t^u, v) = \sum_{i \in \{x, y, w, h\}}smooth_{L_1}(t^u_i - v_i)$
  • All experiments use $\lambda = 1$

mini-bath sampling

  • batch-size = 128
  • sampling 64 RoIs from each image
  • images are horizontally flipped with probability 0.5

back-propagation through RoI pooling layers

backwards function:

SGD hyper-parameters

softmax classification and bounding-box regression are initialized from zero-mean Gaussian distributions with standard deviations 0.01 and 0.001

biases are initialized to 0

scale invariance

two ways:

  • “brute force” learning
  • image pyramids

Fast R-CNN Detection

truncated SVD for faster detection

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the time cost on FC is nearly half of the forward pass time

  • w is u * v
  • U is v * t
  • $\Sigma_t$ is t * t
  • V is v * t
  • truncated SVD reduces the parameter count from uv to t(u + v) $t \ll min(u, v)$

Main Results

three main results:

  • state-of-the-art mAP on VOC 2007, 2010, and 2012
  • fast training and testing compared to R-CNN, SPPnet
  • fine-tuning conv layers in VGG16 improves mAP

VOC 2010 and 2012 results

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VOC 2007 results

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training and testing time

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truncated SVD

truncated SVD can reduce detection time by more than 30% with only a small (0.3 percentage point) drop in mAP

which layers to fine-tune

validate that fine-tuning the conv layers is important for VGG16

training through the RoI pooling layer is important for very deep nets

Alt text

Question:

  • Does this mean that all conv layers should be fine-tuned?

Answer:

  • no
  • lower layer is generic and task independent has no meaningful effect on mAP

Design Evaluation

dose multi-task training help

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scale invariance: to brute force or finesse?

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deep ConvNets are adept at directly learning scale invariance

do we need more training data?

A good object detector should improve when supplied with more training data

  • roughly tripling the number of images to 16.5k
  • improves mAP on VOC07 test from 66.9% to 70.0%

do SVMs outperform softmax?

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Reason:

  • softmax, unlike one-vs-rest SVMs, introduces competition between classes when scoring a RoI

are more proposals always better?

  • sparse set of object proposals
  • dense set

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  • that swamping the deep classifier with more proposals does not help, and even slightly hurts, accuracy

preliminary MS COCO results

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Conclusion

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Reference

11. K.He,X.Zhang,S.Ren,andJ.Sun.Spatial pyramid pooling in deep convolutional networks for visual recognition. In ECCV,2014