Fast R-CNN
Ross Girshick
Microsoft Research
rbg@microsoft.com

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 9× faster than R-CNN, is 213× fasterat test-time, and achieves a higher mAP on PASCAL VOC
2012. Compared to SPPnet, Fast R-CNN trains VGG16 3×faster, tests 10× faster, and is more accurate. Fast R-CNN
is implemented in Python and C++ (using Caffe) and isavailable under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.

while achieving top accuracy on PASCAL VOC 2012 [7]with a mAP of 66% (vs. 62% for R-CNN).1

1.1. R-CNN and SPPnet
The Region-based Convolutional Network method (RCNN) [9] achieves excellent object detection accuracy byusing a deep ConvNet to classify object proposals. R-CNN,
however, has notable drawbacks:
1. Training is a multi-stage pipeline. R-CNN first finetunes a ConvNet on object proposals using log loss.
Then, it fits SVMs to ConvNet features. These SVMs
act as object detectors, replacing the softmax classifier learnt by fine-tuning. In the third training stage,bounding-box regressors are learned.
2. Training is expensive in space and time. For SVM
and bounding-box regressor training, features are extracted from each object proposal in each image andwritten to disk. With very deep networks, such as
VGG16, this process takes 2.5 GPU-days for the 5k
images of the VOC07 trainval set. These features require hundreds of gigabytes of storage.

1. Introduction
Recently, deep ConvNets [14, 16] have significantly improved image classification [14] and object detection [9, 19]
accuracy. Compared to image classification, object detection is a more challenging task that requires more complex methods to solve. Due to this complexity, current approaches (e.g., [9, 11, 19, 25]) train models in multi-stage
pipelines that are slow and inelegant.
Complexity arises because detection requires the accurate localization of objects, creating two primary challenges. First, numerous candidate object locations (often
called “proposals”) must be processed. Second, these candidates provide only rough localization that must be refinedto achieve precise localization. Solutions to these problems
often compromise speed, accuracy, or simplicity.
In this paper, we streamline the training process for stateof-the-art ConvNet-based object detectors [9, 11]. We propose a single-stage training algorithm that jointly learns toclassify object proposals and refine their spatial locations.
The resulting method can train a very deep detection
network (VGG16 [20]) 9× faster than R-CNN [9] and 3×faster than SPPnet [11]. At runtime, the detection network
processes images in 0.3s (excluding object proposal time)

3. Object detection is slow. At test-time, features are
extracted from each object proposal in each test image.
Detection with VGG16 takes 47s / image (on a GPU).
R-CNN is slow because it performs a ConvNet forward
pass for each object proposal, without sharing computation.
Spatial pyramid pooling networks (SPPnets) [11] were proposed to speed up R-CNN by sharing computation. The
SPPnet method computes a convolutional feature map forthe entire input image and then classifies each object proposal using a feature vector extracted from the shared feature map. Features are extracted for a proposal by maxpooling the portion of the feature map inside the proposal
into a fixed-size output (e.g., 6 × 6). Multiple output sizes
are pooled and then concatenated as in spatial pyramid pooling [15]. SPPnet accelerates R-CNN by 10 to 100× at test
time. Training time is also reduced by 3× due to faster proposal feature extraction.
1 All timings use one Nvidia K40 GPU overclocked to 875 MHz.

1440

SPPnet also has notable drawbacks. Like R-CNN, training is a multi-stage pipeline that involves extracting features, fine-tuning a network with log loss, training SVMs,
and finally fitting bounding-box regressors. Features arealso written to disk. But unlike R-CNN, the fine-tuning algorithm proposed in [11] cannot update the convolutional
layers that precede the spatial pyramid pooling. Unsurprisingly, this limitation (fixed convolutional layers) limits theaccuracy of very deep networks.

1.2. Contributions
We propose a new training algorithm that fixes the disadvantages of R-CNN and SPPnet, while improving on their
speed and accuracy. We call this method Fast R-CNN because it’s comparatively fast to train and test. The Fast RCNN method has several advantages:
1. Higher detection quality (mAP) than R-CNN, SPPnet
2. Training is single-stage, using a multi-task loss
3. Training can update all network layers
4. No disk storage is required for feature caching
Fast R-CNN is written in Python and C++ (Caffe[13]) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.

Outputs:bbox
softmax regressor

Deep
ConvNetRoI
projection

Conv
feature map

RoIpooling
layer

FC

FC

FCs

RoI feature
vector For each RoI

Figure 1. Fast R-CNN architecture. An input image and multiple regions of interest (RoIs) are input into a fully convolutional
network. Each RoI is pooled into a fixed-size feature map andthen mapped to a feature vector by fully connected layers (FCs).
The network has two output vectors per RoI: softmax probabilities
and per-class bounding-box regression offsets. The architecture istrained end-to-end with a multi-task loss.

RoI max pooling works by dividing the h × w RoI window into an H × W grid of sub-windows of approximate
size h/H × w/W and then max-pooling the values in each
sub-window into the corresponding output grid cell. Pooling is applied independently to each feature map channel,
as in standard max pooling. 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. We
use the pooling sub-window calculation given in [11].

2.2. Initializing from pre-trained networks

Fig. 1 illustrates the Fast R-CNN architecture. A Fast
R-CNN network takes as input an entire image and a setof object proposals. The network first processes the whole
image with several convolutional (conv) and max poolinglayers to produce a conv feature map. Then, 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(fc) layers that finally branch into two sibling output layers: one that produces softmax probability estimates over
K object classes plus a catch-all “background” class andanother layer that outputs four real-valued numbers for eachof the K object classes. Each set of 4 values encodes refinedbounding-box positions for one of the K classes.

We experiment with three pre-trained ImageNet [4] networks, each with five max pooling layers and between fiveand thirteen conv layers (see Section 4.1 for network details). When a pre-trained network initializes a Fast R-CNN
network, it undergoes three transformations.
First, the last max pooling layer is replaced by a RoI
pooling layer that is configured by setting H and W to becompatible with the net’s first fully connected layer (e.g.,
H = W = 7 for VGG16).
Second, the network’s last fully connected layer and softmax (which were trained for 1000-way ImageNet classification) are replaced with the two sibling layers describedearlier (a fully connected layer and softmax over K + 1 categories and category-specific bounding-box regressors).
Third, the network is modified to take two data inputs: alist of images and a list of RoIs in those images.

2.1. The RoI pooling layer

2.3. Fine-tuning for detection

The RoI pooling layer uses max pooling to convert thefeatures inside any valid region of interest into a small feature map with a fixed spatial extent of H × W (e.g., 7 × 7),
where H and W are layer hyper-parameters that are independent of any particular RoI. In this paper, an RoI is arectangular window into a conv feature map. Each RoI is
defined by a four-tuple (r, c, h, w) that specifies its top-leftcorner (r, c) and its height and width (h, w).

Training all network weights with back-propagation is an
important capability of Fast R-CNN. First, let’s elucidate
why SPPnet is unable to update weights below the spatial
pyramid pooling layer.
The root cause is that back-propagation through the SPP
layer is highly inefficient when each training sample (i.e.
RoI) comes from a different image, which is exactly how
R-CNN and SPPnet networks are trained. The inefficiency

2. Fast R-CNN architecture and training

1441

stems from the fact that each RoI may have a very large
receptive field, often spanning the entire input image. Since
the forward pass must process the entire receptive field, thetraining inputs are large (often the entire image).
We propose a more efficient training method that takes
advantage of feature sharing during training. In Fast RCNN training, stochastic gradient descent (SGD) minibatches are sampled hierarchically, first by sampling N images and then by sampling R/N RoIs from each image.
Critically, RoIs from the same image share computationand memory in the forward and backward passes. Making
N small decreases mini-batch computation. For example,
when using N = 2 and R = 128, the proposed training
scheme is roughly 64× faster than sampling one RoI from
128 different images (i.e., the R-CNN and SPPnet strategy).
One concern over this strategy is it may cause slow training convergence because RoIs from the same image are correlated. This concern does not appear to be a practical issue
and we achieve good results with N = 2 and R = 128
using fewer SGD iterations than R-CNN.
In addition to hierarchical sampling, Fast R-CNN uses a
streamlined training process with one fine-tuning stage thatjointly optimizes a softmax classifier and bounding-box regressors, rather than training a softmax classifier, SVMs,
and regressors in three separate stages [9, 11]. The components of this procedure (the loss, mini-batch sampling strategy, back-propagation through RoI pooling layers, and SGD
hyper-parameters) are described below.
Multi-task loss. A Fast R-CNN network has two sibling
output layers. The first outputs a discrete probability distribution (per RoI), p = (p0 , . . . , pK ), over K + 1 categories.
As usual, p is computed by a softmax over the K +1 outputsof a fully connected layer. The second sibling layer outputs
bounding-box regression offsets, tk = tkx , tky , tkw , tkh , foreach of the K object classes, indexed by k. We use the parameterization for tk given in [9], in which tk specifies ascale-invariant translation and log-space height/width shift
relative to an object proposal.
Each training RoI is labeled with a ground-truth class u
and a ground-truth bounding-box regression target v. We
use a multi-task loss L on each labeled RoI to jointly train
for classification and bounding-box regression:L(p, u, tu , v) = Lcls (p, u) + λ[u ≥ 1]Lloc (tu , v),(1)

in which Lcls (p, u) = − log pu is log loss for true class u.
The second task loss, Lloc , is defined over a tuple oftrue bounding-box regression targets for class u, v =(vx , vy , vw , vh ), and a predicted tuple tu = (tux , tuy , tuw , tuh ),again for class u. The Iverson bracket indicator function[u ≥ 1] evaluates to 1 when u ≥ 1 and 0 otherwise. By
convention the catch-all background class is labeled u = 0.
For background RoIs there is no notion of a ground-truth

bounding box and hence Lloc is ignored. For bounding-box
regression, we use the loss
X
Lloc (tu , v) =smoothL1 (tui − vi ),(2)i∈{x,y,w,h}in whichsmoothL1 (x) =

(

0.5x2
if |x| < 1|x| − 0.5 otherwise,(3)

is a robust L1 loss that is less sensitive to outliers than theL2 loss used in R-CNN and SPPnet. When the regression
targets are unbounded, training with L2 loss can require
careful tuning of learning rates in order to prevent exploding
gradients. Eq. 3 eliminates this sensitivity.
The hyper-parameter λ in Eq. 1 controls the balance between the two task losses. We normalize the ground-truth
regression targets vi to have zero mean and unit variance.
All experiments use λ = 1.
We note that [6] uses a related loss to train a classagnostic object proposal network. Different from our approach, [6] advocates for a two-network system that separates localization and classification. OverFeat [19], R-CNN[9], and SPPnet [11] also train classifiers and bounding-box
localizers, however these methods use stage-wise training,which we show is suboptimal for Fast R-CNN (Section 5.1).
Mini-batch sampling. During fine-tuning, each SGD
mini-batch is constructed from N = 2 images, chosen uniformly at random (as is common practice, we actually iterate over permutations of the dataset). We use mini-batchesof size R = 128, sampling 64 RoIs from each image. Asin [9], we take 25% of the RoIs from object proposals thathave intersection over union (IoU) overlap with a groundtruth bounding box of at least 0.5. These RoIs comprisethe examples labeled with a foreground object class, i.e.
u ≥ 1. The remaining RoIs are sampled from object proposals that have a maximum IoU with ground truth in the interval [0.1, 0.5), following [11]. These are the background
examples and are labeled with u = 0. The lower thresholdof 0.1 appears to act as a heuristic for hard example mining[8]. During training, images are horizontally flipped withprobability 0.5. No other data augmentation is used.
Back-propagation through RoI pooling layers. Backpropagation routes derivatives through the RoI poolinglayer. For clarity, we assume only one image per mini-batch(N = 1), though the extension to N > 1 is straightforward
because the forward pass treats all images independently.
Let xi ∈ R be the i-th activation input into the RoI pooling layer and let yrj be the layer’s j-th output from the rth RoI. The RoI pooling layer computes yrj = xi∗ (r,j) , inwhich i∗ (r, j) = argmaxi′ ∈R(r,j) xi′ . R(r, j) is the index1442

set of inputs in the sub-window over which the output unit
yrj max pools. A single xi may be assigned to several different outputs yrj .
The RoI pooling layer’s backwards function computes
partial derivative of the loss function with respect to each
input variable xi by following the argmax switches:XX∂L∂L[i = i∗ (r, j)]
=
.
∂xi
∂y
rj
r
j

(4)

In words, for each mini-batch RoI r and for each pooling
output unit yrj , the partial derivative ∂L/∂yrj is accumulated if i is the argmax selected for yrj by max pooling.
In back-propagation, the partial derivatives ∂L/∂yrj are already computed by the backwards function of the layer
on top of the RoI pooling layer.
SGD hyper-parameters. The fully connected layers used
for softmax classification and bounding-box regression are
initialized from zero-mean Gaussian distributions with standard deviations 0.01 and 0.001, respectively. Biases are initialized to 0. All layers use a per-layer learning rate of 1 forweights and 2 for biases and a global learning rate of 0.001.
When training on VOC07 or VOC12 trainval we run SGD
for 30k mini-batch iterations, and then lower the learning
rate to 0.0001 and train for another 10k iterations. When
we train on larger datasets, we run SGD for more iterations,as described later. A momentum of 0.9 and parameter decayof 0.0005 (on weights and biases) are used.

2.4. Scale invariance
We explore two ways of achieving scale invariant object detection: (1) via “brute force” learning and (2) by using image pyramids. These strategies follow the two approaches in [11]. In the brute-force approach, each image
is processed at a pre-defined pixel size during both training
and testing. The network must directly learn scale-invariant
object detection from the training data.
The multi-scale approach, in contrast, provides approximate scale-invariance to the network through an image
pyramid. At test-time, the image pyramid is used to approximately scale-normalize each object proposal. Duringmulti-scale training, we randomly sample a pyramid scaleeach time an image is sampled, following [11], as a form ofdata augmentation. We experiment with multi-scale training for smaller networks only, due to GPU memory limits.

test-time, R is typically around 2000, although we will consider cases in which it is larger (≈ 45k). When using animage pyramid, each RoI is assigned to the scale such thatthe scaled RoI is closest to 2242 pixels in area [11].
For each test RoI r, the forward pass outputs a classposterior probability distribution p and a set of predictedbounding-box offsets relative to r (each of the K classes
gets its own refined bounding-box prediction). We assign a
detection confidence to r for each object class k using the∆estimated probability Pr(class = k | r) = pk . We thenperform non-maximum suppression independently for eachclass using the algorithm and settings from R-CNN [9].

3.1. Truncated SVD for faster detection
For whole-image classification, the time spent computing the fully connected layers is small compared to the conv
layers. On the contrary, for detection the number of RoIs
to process is large and nearly half of the forward pass timeis spent computing the fully connected layers (see Fig. 2).
Large fully connected layers are easily accelerated by compressing them with truncated SVD [5, 23].
In this technique, a layer parameterized by the u × v
weight matrix W is approximately factorized as
W ≈ U Σt V T(5)using SVD. In this factorization, U is a u × t matrix comprising the first t left-singular vectors of W , Σt is a t × t
diagonal matrix containing the top t singular values of W ,and V is v × t matrix comprising the first t right-singular
vectors of W . Truncated SVD reduces the parameter countfrom uv to t(u + v), which can be significant if t is muchsmaller than min(u, v). To compress a network, the single
fully connected layer corresponding to W is replaced by
two fully connected layers, without a non-linearity between
them. The first of these layers uses the weight matrix Σt V T(and no biases) and the second uses U (with the original biases associated with W ). This simple compression method
gives good speedups when the number of RoIs is large.

4. Main results
Three main results support this paper’s contributions:
1. State-of-the-art mAP on VOC07, 2010, and 2012
2. Fast training and testing compared to R-CNN, SPPnet
3. Fine-tuning conv layers in VGG16 improves mAP

3. Fast R-CNN detection
Once a Fast R-CNN network is fine-tuned, detection
amounts to little more than running a forward pass (assuming object proposals are pre-computed). The network takes
as input an image (or an image pyramid, encoded as a listof images) and a list of R object proposals to score. At

4.1. Experimental setup
Our experiments use three pre-trained ImageNet models
that are available online.2 The first is the CaffeNet (essentially AlexNet [14]) from R-CNN [9]. We alternatively refer
2 https://github.com/BVLC/caffe/wiki/Model-Zoo

1443

SPPnet BB [11]R-CNN BB [10]train set aero bike bird boat bottle bus car cat chair cow table dog horse mbike persn plant sheep sofa train tv mAP
07 \ diff 73.9 72.3 62.5 51.5 44.4 74.4 73.0 74.4 42.3 73.6 57.7 70.3 74.6 74.3 54.2 34.0 56.4 56.4 67.9 73.5 63.1
73.4 77.0 63.4 45.4 44.6 75.1 78.1 79.8 40.5 73.7 62.2 79.4 78.1 73.1 64.2 35.6 66.8 67.2 70.4 71.1 66.0
07

FRCN [ours]
FRCN [ours]
FRCN [ours]

74.5 78.3 69.2 53.2 36.6 77.3 78.2 82.0 40.7 72.7 67.9 79.6 79.2
07
07 \ diff 74.6 79.0 68.6 57.0 39.3 79.5 78.6 81.9 48.0 74.0 67.4 80.5 80.7
77.0 78.1 69.3 59.4 38.3 81.6 78.6 86.7 42.8 78.8 68.9 84.7 82.0
07+12

method
†

73.0

69.0

30.1

65.4

70.2 75.8 65.8 66.9

74.1

69.6

31.8

67.1

68.4 75.3 65.5 68.1

76.6

69.9

31.8

70.1

74.8 80.4 70.4 70.0

Table 1. VOC 2007 test detection average precision (%). All methods use VGG16. Training set key: 07: VOC07 trainval, 07 \ diff: 07
without “difficult” examples, 07+12: union of 07 and VOC12 trainval. † SPPnet results were prepared by the authors of [11].

BabyLearning
R-CNN BB [10]SegDeepM

train set aero bike bird boat bottle bus car cat chair cow table dog horse mbike persn plant sheep sofa train tv
77.7 73.8 62.3 48.8 45.4 67.3 67.0 80.3 41.3 70.8 49.7 79.5 74.7 78.6 64.5 36.0 69.9 55.7 70.4 61.7
Prop.
79.3 72.4 63.1 44.0 44.4 64.6 66.3 84.9 38.8 67.3 48.4 82.3 75.0 76.7 65.7 35.8 66.2 54.8 69.1 58.8
12
12+seg 82.3 75.2 67.1 50.7 49.8 71.1 69.6 88.2 42.5 71.2 50.0 85.7 76.6 81.8 69.3 41.5 71.9 62.2 73.2 64.6

FRCN [ours]
FRCN [ours]

12
07++12

method80.1 74.4 67.7 49.4 41.4 74.2 68.8 87.8 41.9 70.1 50.2 86.1 77.3
82.0 77.8 71.6 55.3 42.4 77.3 71.7 89.3 44.5 72.1 53.7 87.7 80.0

mAP
63.8
62.9
67.2

81.1

70.4

33.3

67.0

63.3 77.2 60.0 66.1

82.5

72.7

36.6

68.7

65.4 81.1 62.7 68.8

Table 2. VOC 2010 test detection average precision (%). BabyLearning uses a network based on [17]. All other methods use VGG16.
Training set key: 12: VOC12 trainval, Prop.: proprietary dataset, 12+seg: 12 with segmentation annotations, 07++12: union of VOC07
trainval, VOC07 test, and VOC12 trainval.
methodtrain set aero bike bird boat bottle bus car cat chair cow table dog horse mbike persn plant sheep sofa train tv
78.0 74.2 61.3 45.7 42.7 68.2 66.8 80.2 40.6 70.0 49.8 79.0 74.5 77.9 64.0 35.3 67.9 55.7 68.7 62.6
80.2 73.8 61.9 43.7 43.0 70.3 67.6 80.7 41.9 69.7 51.7 78.2 75.2 76.9 65.1 38.6 68.3 58.0 68.7 63.3
79.6 72.7 61.9 41.2 41.9 65.9 66.4 84.6 38.5 67.2 46.7 82.0 74.8 76.0 65.2 35.6 65.4 54.2 67.4 60.3

BabyLearning
Prop.
NUS NIN c2000 Unk.
R-CNN BB [10] 12
FRCN [ours]
FRCN [ours]

12
07++1280.3 74.7 66.9 46.9 37.7 73.9 68.6 87.7 41.7 71.1 51.1 86.0 77.8
82.3 78.4 70.8 52.3 38.7 77.8 71.6 89.3 44.2 73.0 55.0 87.5 80.5

mAP
63.2
63.8
62.4

79.8

69.8

32.1

65.5

63.8 76.4 61.7 65.7

80.8

72.0

35.1

68.3

65.7 80.4 64.2 68.4

Table 3. VOC 2012 test detection average precision (%). BabyLearning and NUS NIN c2000 use networks based on [17]. All othermethods use VGG16. Training set key: see Table 2, Unk.: unknown.

to this CaffeNet as model S, for “small.” The second network is VGG CNN M 1024 from [3], which has the same
depth as S, but is wider. We call this network model M,
for “medium.” The final network is the very deep VGG16
model from [20]. Since this model is the largest, we callit model L. In this section, all experiments use single-scaletraining and testing (s = 600; see Section 5.2 for details).

4.2. VOC 2010 and 2012 results
On these datasets, we compare Fast R-CNN (FRCN, forshort) against the top methods on the comp4 (outside data)
track from the public leaderboard (Table 2, Table 3).3 For
the NUS NIN c2000 and BabyLearning methods, there areno associated publications at this time and we could not
find exact information on the ConvNet architectures used;
they are variants of the Network-in-Network design [17].
All other methods are initialized from the same pre-trained
VGG16 network.
Fast R-CNN achieves the top result on VOC12 with amAP of 65.7% (and 68.4% with extra data). It is also twoorders of magnitude faster than the other methods, which
are all based on the “slow” R-CNN pipeline. On VOC10,
3 http://host.robots.ox.ac.uk:8080/leaderboard(accessed April 18, 2015)

SegDeepM [25] achieves a higher mAP than Fast R-CNN(67.2% vs. 66.1%). SegDeepM is trained on VOC12 trainval plus segmentation annotations; it is designed to boost
R-CNN accuracy by using a Markov random field to reason
over R-CNN detections and segmentations from the O2 P
[1] semantic-segmentation method. Fast R-CNN can beswapped into SegDeepM in place of R-CNN, which maylead to better results. When using the enlarged 07++12
training set (see Table 2 caption), Fast R-CNN’s mAP increases to 68.8%, surpassing SegDeepM.

4.3. VOC 2007 results
On VOC07, we compare Fast R-CNN to R-CNN and
SPPnet. All methods start from the same pre-trained
VGG16 network and use bounding-box regression. The
VGG16 SPPnet results were computed by the authors of[11]. SPPnet uses five scales during both training and testing. The improvement of Fast R-CNN over SPPnet illustrates that even though Fast R-CNN uses single-scale training and testing, fine-tuning the conv layers provides a large
improvement in mAP (from 63.1% to 66.9%). R-CNN
achieves a mAP of 66.0%. As a minor point, SPPnet wastrained without examples marked as “difficult” in PASCAL.
Removing these examples improves Fast R-CNN mAP to68.1%. All other experiments use “difficult” examples.

1444

4.4. Training and testing time

4.5. Which layers to fine-tune?

Fast training and testing times are our second main result. Table 4 compares training time (hours), testing rate(seconds per image), and mAP on VOC07 between Fast RCNN, R-CNN, and SPPnet. For VGG16, Fast R-CNN processes images 146× faster than R-CNN without truncated
SVD and 213× faster with it. Training time is reduced by
9×, from 84 hours to 9.5. Compared to SPPnet, Fast RCNN trains VGG16 2.7× faster (in 9.5 vs. 25.5 hours) andtests 7× faster without truncated SVD or 10× faster with it.
Fast R-CNN also eliminates hundreds of gigabytes of disk
storage, because it does not cache features.

For the less deep networks considered in the SPPnet paper [11], fine-tuning only the fully connected layers appeared to be sufficient for good accuracy. We hypothesizedthat this result would not hold for very deep networks. To
validate that fine-tuning the conv layers is important for
VGG16, we use Fast R-CNN to fine-tune, but freeze thethirteen conv layers so that only the fully connected layers
learn. This ablation emulates single-scale SPPnet training
and decreases mAP from 66.9% to 61.4% (Table 5). This
experiment verifies our hypothesis: training through the RoI
pooling layer is important for very deep nets.

Fast R-CNN
S
M

LSPPnet
†
L

84
1×

25
3.4×

9.8 12.1 47.0
-

2.3
-

1×
-

1×
-

20×
-

66.9 58.5 60.2 66.0
66.6
-

63.1
-

L

1.2
2.0
9.5
18.3× 14.0× 8.8×

train time (h)train speedup
test rate (s/im)⊲ with SVD

0.10
0.06

0.15
0.08

test speedup⊲ with SVD

98× 80× 146×
169× 150× 213×

VOC07 mAP
⊲ with SVD

57.1
56.5

59.2
58.7

0.32
0.22

R-CNN
S M
22
1×

28
1×

1×
-

Table 4. Runtime comparison between the same models in Fast RCNN, R-CNN, and SPPnet. Fast R-CNN uses single-scale mode.
SPPnet uses the five scales specified in [11]. † Timing provided by
the authors of [11]. Times were measured on an Nvidia K40 GPU.

Truncated SVD. Truncated SVD can reduce detection
time by more than 30% with only a small (0.3 percentage point) drop in mAP and without needing to perform
additional fine-tuning after model compression. Fig. 2 illustrates how using the top 1024 singular values from the25088 × 4096 matrix in VGG16’s fc6 layer and the top 256
singular values from the 4096 × 4096 fc7 layer reduces runtime with little loss in mAP. Further speed-ups are possible with smaller drops in mAP if one fine-tunes again after
compression.

Forward pass timing
mAP 66.9% @ 320ms / image

Forward pass timing (SVD)mAP 66.6% @ 223ms / image
fc6

fc6

17.5% (37ms) other
5.1% (11ms)
roi_pool5
7.9% (17ms)
1.7% (4ms) fc7

38.7% (122ms)

other3.5% (11ms) roi_pool5
5.4% (17ms)
6.2% (20ms) fc7

46.3% (146ms)
conv

67.8% (143ms)
conv

Figure 2. Timing for VGG16 before and after truncated SVD. Before SVD, fully connected layers fc6 and fc7 take 45% of the time.

layers that are fine-tuned in model L SPPnet L≥ fc6 ≥ conv3 1≥ conv2 1≥ fc6
VOC07 mAP
61.4
66.9
67.2
63.1
0.32
0.32
2.3
test rate (s/im) 0.32
Table 5. Effect of restricting which layers are fine-tuned for
VGG16. Fine-tuning ≥ fc6 emulates the SPPnet training algorithm [11], but using a single scale. SPPnet L results were obtained using five scales, at a significant (7×) speed cost.

Does this mean that all conv layers should be fine-tuned?
In short, no. In the smaller networks (S and M) we findthat conv1 is generic and task independent (a well-known
fact [14]). Allowing conv1 to learn, or not, has no meaningful effect on mAP. For VGG16, we found it only necessary to update layers from conv3 1 and up (9 of the 13
conv layers). This observation is pragmatic: (1) updatingfrom conv2 1 slows training by 1.3× (12.5 vs. 9.5 hours)
compared to learning from conv3 1; and (2) updating fromconv1 1 over-runs GPU memory. The difference in mAP
when learning from conv2 1 up was only +0.3 points (Table 5, last column). All Fast R-CNN results in this paperusing VGG16 fine-tune layers conv3 1 and up; all experiments with models S and M fine-tune layers conv2 and up.

5. Design evaluation
We conducted experiments to understand how Fast RCNN compares to R-CNN and SPPnet, as well as to evaluate design decisions. Following best practices, we performed these experiments on the PASCAL VOC07 dataset.

5.1. Does multi-task training help?
Multi-task training is convenient because it avoids managing a pipeline of sequentially-trained tasks. But it also has
the potential to improve results because the tasks influenceeach other through a shared representation (the ConvNet)[2]. Does multi-task training improve object detection accuracy in Fast R-CNN?
To test this question, we train baseline networks thatuse only the classification loss, Lcls , in Eq. 1 (i.e., setting

1445

S
multi-task training?
stage-wise training?
test-time bbox reg?
VOC07 mAP

M

X

52.2

53.3

X
X
X
54.6

X
57.1

L

X

54.7

55.5

X
X
X
56.6

X
59.2

X

62.6

63.4

X
X
X
64.0

X
66.9

Table 6. Multi-task training (forth column per group) improves mAP over piecewise training (third column per group).

λ = 0). These baselines are printed for models S, M, and L
in the first column of each group in Table 6. Note that thesemodels do not have bounding-box regressors. Next (second
column per group), we take networks that were trained withthe multi-task loss (Eq. 1, λ = 1), but we disable boundingbox regression at test time. This isolates the networks’ classification accuracy and allows an apples-to-apples comparison with the baseline networks.
Across all three networks we observe that multi-task
training improves pure classification accuracy relative totraining for classification alone. The improvement ranges
from +0.8 to +1.1 mAP points, showing a consistent positive effect from multi-task learning.
Finally, we take the baseline models (trained with only
the classification loss), tack on the bounding-box regression
layer, and train them with Lloc while keeping all other network parameters frozen. The third column in each groupshows the results of this stage-wise training scheme: mAP
improves over column one, but stage-wise training underperforms multi-task training (forth column per group).

5.2. Scale invariance: to brute force or finesse?
We compare two strategies for achieving scale-invariant
object detection: brute-force learning (single scale) and image pyramids (multi-scale). In either case, we define thescale s of an image to be the length of its shortest side.
All single-scale experiments use s = 600 pixels; s may
be less than 600 for some images as we cap the longest image side at 1000 pixels and maintain the image’s aspect ratio. These values were selected so that VGG16 fits in GPU
memory during fine-tuning. The smaller models are notmemory bound and can benefit from larger values of s; however, optimizing s for each model is not our main concern.
We note that PASCAL images are 384 × 473 pixels on average and thus the single-scale setting typically upsamples
images by a factor of 1.6. The average effective stride at the
RoI pooling layer is thus ≈ 10 pixels.
In the multi-scale setting, we use the same five scales
specified in [11] (s ∈ {480, 576, 688, 864, 1200}) to facilitate comparison with SPPnet. However, we cap the longest
side at 2000 pixels to avoid exceeding GPU memory.
Table 7 shows models S and M when trained and tested
with either one or five scales. Perhaps the most surprising result in [11] was that single-scale detection performs
almost as well as multi-scale detection. Our findings con-scalestest rate (s/im)
VOC07 mAP

SPPnet ZF
1
5
0.14 0.38
58.0 59.2

S
1
0.10
57.1

M
5
0.39
58.4

1
0.15
59.2

5
0.64
60.7

L
1
0.32
66.9

Table 7. Multi-scale vs. single scale. SPPnet ZF (similar to modelS) results are from [11]. Larger networks with a single-scale offer
the best speed / accuracy tradeoff. (L cannot use multi-scale in our
implementation due to GPU memory constraints.)

firm their result: deep ConvNets are adept at directly learning scale invariance. The multi-scale approach offers onlya small increase in mAP at a large cost in compute time
(Table 7). In the case of VGG16 (model L), we are limited to using a single scale by implementation details. Yet it
achieves a mAP of 66.9%, which is slightly higher than the
66.0% reported for R-CNN [10], even though R-CNN uses
“infinite” scales in the sense that each proposal is warped toa canonical size.
Since single-scale processing offers the best tradeoff between speed and accuracy, especially for very deep models,
all experiments outside of this sub-section use single-scaletraining and testing with s = 600 pixels.

5.3. Do we need more training data?
A good object detector should improve when suppliedwith more training data. Zhu et al. [24] found that DPM [8]mAP saturates after only a few hundred to thousand training examples. Here we augment the VOC07 trainval set
with the VOC12 trainval set, roughly tripling the numberof images to 16.5k, to evaluate Fast R-CNN. Enlarging thetraining set improves mAP on VOC07 test from 66.9% to70.0% (Table 1). When training on this dataset we use 60k
mini-batch iterations instead of 40k.
We perform similar experiments for VOC10 and 2012,
for which we construct a dataset of 21.5k images from theunion of VOC07 trainval, test, and VOC12 trainval. Whentraining on this dataset, we use 100k SGD iterations andlower the learning rate by 0.1× each 40k iterations (insteadof each 30k). For VOC10 and 2012, mAP improves from
66.1% to 68.8% and from 65.7% to 68.4%, respectively.

5.4. Do SVMs outperform softmax?
Fast R-CNN uses the softmax classifier learnt duringfine-tuning instead of training one-vs-rest linear SVMs

1446

post-hoc, as was done in R-CNN and SPPnet. To understand the impact of this choice, we implemented post-hoc
SVM training with hard negative mining in Fast R-CNN.
We use the same training algorithm and hyper-parameters
as in R-CNN.
methodR-CNN [9, 10]
FRCN [ours]
FRCN [ours]

classifier
SVM
SVM
softmax

S
58.5
56.3
57.1

M
60.2
58.7
59.2

L
66.0
66.8
66.9

Table 8. Fast R-CNN with softmax vs. SVM (VOC07 mAP).

Table 8 shows softmax slightly outperforming SVM for
all three networks, by +0.1 to +0.8 mAP points. This effect is small, but it demonstrates that “one-shot” fine-tuning
is sufficient compared to previous multi-stage training approaches. We note that softmax, unlike one-vs-rest SVMs,
introduces competition between classes when scoring a RoI.

5.5. Are more proposals always better?
There are (broadly) two types of object detectors: thosethat use a sparse set of object proposals (e.g., selective
search [21]) and those that use a dense set (e.g., DPM [8]).
Classifying sparse proposals is a type of cascade [22] inwhich the proposal mechanism first rejects a vast number ofcandidates leaving the classifier with a small set to evaluate.
This cascade improves detection accuracy when applied to
DPM detections [21]. We find evidence that the proposalclassifier cascade also improves Fast R-CNN accuracy.
Using selective search’s quality mode, we sweep from 1k
to 10k proposals per image, each time re-training and retesting model M. If proposals serve a purely computationalrole, increasing the number of proposals per image should
not harm mAP.
66

56

66
Sel. Search (SS)SS (2k) + Rand Dense 63
SS replace Dense
45k Dense Softmax 61
45k Dense SVM
58
SS Avg. Recall
56

53

53

51

51

63

Average Recall

61

mAP

58

49 3
10

104

Number of object proposals

49

Figure 3. VOC07 test mAP and AR for various proposal schemes.

We find that mAP rises and then falls slightly as the proposal count increases (Fig. 3, solid blue line). This experiment shows that swamping the deep classifier with more
proposals does not help, and even slightly hurts, accuracy.

This result is difficult to predict without actually runningthe experiment. The state-of-the-art for measuring object
proposal quality is Average Recall (AR) [12]. AR correlates
well with mAP for several proposal methods using R-CNN,
when using a fixed number of proposals per image. Fig. 3
shows that AR (solid red line) does not correlate well withmAP as the number of proposals per image is varied. AR
must be used with care; higher AR due to more proposals
does not imply that mAP will increase. Fortunately, training
and testing with model M takes less than 2.5 hours. Fast
R-CNN thus enables efficient, direct evaluation of object
proposal mAP, which is preferable to proxy metrics.
We also investigate Fast R-CNN when using densely
generated boxes (over scale, position, and aspect ratio), at
a rate of about 45k boxes / image. This dense set is richenough that when each selective search box is replaced byits closest (in IoU) dense box, mAP drops only 1 point (to57.7%, Fig. 3, blue triangle).
The statistics of the dense boxes differ from those of
selective search boxes. Starting with 2k selective searchboxes, we test mAP when adding a random sample of1000 × {2, 4, 6, 8, 10, 32, 45} dense boxes. For each experiment we re-train and re-test model M. When these denseboxes are added, mAP falls more strongly than when adding
more selective search boxes, eventually reaching 53.0%.
We also train and test Fast R-CNN using only denseboxes (45k / image). This setting yields a mAP of 52.9%(blue diamond). Finally, we check if SVMs with hard negative mining are needed to cope with the dense box distribution. SVMs do even worse: 49.3% (blue circle).

5.6. Preliminary MS COCO results
We applied Fast R-CNN (with VGG16) to the MS
COCO dataset [18] to establish a preliminary baseline. We
trained on the 80k image training set for 240k iterations andevaluated on the “test-dev” set using the evaluation server.
The PASCAL-style mAP is 35.9%; the new COCO-styleAP, which also averages over IoU thresholds, is 19.7%.

6. Conclusion
This paper proposes Fast R-CNN, a clean and fast update
to R-CNN and SPPnet. In addition to reporting state-of-theart detection results, we present detailed experiments that
we hope provide new insights. Of particular note, sparse
object proposals appear to improve detector quality. Thisissue was too costly (in time) to probe in the past, but becomes practical with Fast R-CNN. Of course, there may exist yet undiscovered techniques that allow dense boxes toperform as well as sparse proposals. Such methods, if developed, may help further accelerate object detection.
Acknowledgements. I thank Kaiming He, Larry Zitnick,and Piotr Dollár for helpful discussions and encouragement.

1447

References[1] J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu. Semantic segmentation with second-order pooling. In ECCV,2012. 5[2] R. Caruana. Multitask learning. Machine learning, 28(1),1997. 6[3] K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman.
Return of the devil in the details: Delving deep into convolutional nets. In BMVC, 2014. 5[4] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. FeiFei. ImageNet: A large-scale hierarchical image database.
In CVPR, 2009. 2[5] E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus.
Exploiting linear structure within convolutional networks for
efficient evaluation. In NIPS, 2014. 4[6] D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov. Scalable
object detection using deep neural networks. In CVPR, 2014.
3[7] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, andA. Zisserman. The PASCAL Visual Object Classes (VOC)
Challenge. IJCV, 2010. 1[8] P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. TPAMI, 2010. 3, 7, 8
[9] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic
segmentation. In CVPR, 2014. 1, 3, 4, 8
[10] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Regionbased convolutional networks for accurate object detection
and segmentation. TPAMI, 2015. 5, 7, 8
[11] K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid poolingin deep convolutional networks for visual recognition. In
ECCV, 2014. 1, 2, 3, 4, 5, 6, 7
[12] J. H. Hosang, R. Benenson, P. Dollár, and B. Schiele. Whatmakes for effective detection proposals? arXiv preprint
arXiv:1502.05082, 2015. 8[13] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutionalarchitecture for fast feature embedding. In Proc. of the ACM
International Conf. on Multimedia, 2014. 2[14] A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS,
2012. 1, 4, 6
[15] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags offeatures: Spatial pyramid matching for recognizing naturalscene categories. In CVPR, 2006. 1[16] Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard,
W. Hubbard, and L. Jackel. Backpropagation applied tohandwritten zip code recognition. Neural Comp., 1989. 1[17] M. Lin, Q. Chen, and S. Yan. Network in network. In ICLR,
2014. 5[18] T. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick,J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft COCO: common objects in context. arXiv
e-prints, arXiv:1405.0312 [cs.CV], 2014. 8[19] P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus,
and Y. LeCun. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks. In ICLR,
2014. 1, 3
[20] K. Simonyan and A. Zisserman. Very deep convolutional
networks for large-scale image recognition. In ICLR, 2015.
1, 5[21] J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders.
Selective search for object recognition. IJCV, 2013. 8[22] P. Viola and M. Jones. Rapid object detection using a boosted
cascade of simple features. In CVPR, 2001. 8[23] J. Xue, J. Li, and Y. Gong. Restructuring of deep neuralnetwork acoustic models with singular value decomposition.
In Interspeech, 2013. 4[24] X. Zhu, C. Vondrick, D. Ramanan, and C. Fowlkes. Do weneed more training data or better models for object detection? In BMVC, 2012. 7[25] Y. Zhu, R. Urtasun, R. Salakhutdinov, and S. Fidler.
segDeepM: Exploiting segmentation and context in deepneural networks for object detection. In CVPR, 2015. 1,
51448