Details of Lazy Deep Learning for Images Recognition in ZZ Photo app

Details of Lazy Deep Learning for Images

Recognition in ZZ Photo app

Artem Chernodub, George Pashchenko

IMMSP NASU

Kharkov AI Club, 20 June 2015.

ZZ Photo

𝑝 (𝑥|𝑦 )=𝑝 ( 𝑦|𝑥 )𝑝 (𝑥)

𝑝 (𝑦 )

Biological-inspired models

Neuroscience

Machine Learning

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Biological Neural Networks

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Artificial Neural Networks

Traditional (Shallow) Neural Networks

Deep Neural Networks

Deep Feedforward Neural Networks

Recurrent Neural Networks

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Conventional Methods vs Deep Learning

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Deep Learning = Learning of Representations (Features)

The traditional model of pattern recognition (since the late 50's):

fixed/engineered features + trainable classifierHand-

crafted Feature

Extractor

TrainableClassifier

Trainable Feature

Extractor

TrainableClassifier

End-to-end learning / Feature learning / Deep learning:

trainable features + trainable classifier

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ImageNet

Le et al. “Building high-level features using large-scale unsupervised learning” ICML 2012.

Model # of parameters

Accuracy, %

Deep Net 10M 15.8

best state-of-the-art N/A 9.3

Training data: 16M images, 20K categories

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Deep Face (Facebook)

Y. Taigman, M. Yang, M.A. Ranzato, L. Wolf. DeepFace: Closing the Gap to Human-Level Performance in Face Verification // CVPR 2014.


Accuracy, %

Deep Face Net 128M 97.35

Human level N/A 97.5

Training data: 4M facial images

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TIMIT Phoneme Recognition

Graves, A., Mohamed, A.-R., and Hinton, G. E. (2013). Speech recognition with deep recurrent neural networks // IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6645–6649. IEEE.

Mohamed, A. and Hinton, G. E. (2010). Phone recognition using restricted Boltzmann machines // IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4354–4357.


Error

Hidden Markov Model, HMM

N / A 27,3%

Deep Belief Network, DBN ~ 4M 26,7%

Deep RNN 4,3M 17.7%Training data: 462 speakers train / 24 speakers test, 3.16 / 0.14 hrs.

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Google Large Vocabulary Speech Recognition

H. Sak, A. Senior, F. Beaufays. Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling // INTERSPEECH’2014.

K. Vesely, A. Ghoshal, L. Burget, D. Povey. Sequence-discriminative training of deep neural networks // INTERSPEECH’2014.


Cross-entropy

ReLU DNN 85M 11.3

Deep Projection LSTM RNN

13M 10.7

Training data: 3M utterances (1900 hrs).

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Classic Feedforward Neural Networks (before 2006).

• Single hidden layer (Kolmogorov-Cybenko Universal Approximation Theorem as the main hope).

• Vanishing gradients effect prevents using more layers.

• Less than 10K free parameters.• Feature preprocessing stage is often critical. 11 /

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Training the traditional (shallow) Neural Network: derivative + optimization

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1) forward propagation pass

),( )1(i

ijij xwfz

),()1(~ )2(j

jj zwgky

where zj is the postsynaptic value for the j-th hidden neuron, w(1) are the hidden layer’s weights, f() are the hidden layer’s activation functions, w(2) are the output layer’s weights, and g() are the output layer’s activation functions.

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2) backpropagation pass

Local gradients calculation:

),1(~)1( kyktOUT.)(' )2( OUT

jjHIDj wzf

,)()2( j

OUT

j

zwkE

.)()1( i

INj

ji

xwkE

Derivatives calculation:

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Bad effect of vanishing (exploding) gradients: a problem

,)( )1()(

)(

m

imjm

ji

zwkE

,' )1()()1()( mi

i

mij

mj

mj wf 0

)()(

mjiwkE=> 1mfor

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Bad effect of vanishing (exploding) gradients: two hypotheses

1) increased frequency and severity of bad localminima

2) pathological curvature, like the type seen in the well-knownRosenbrock function:222 )(100)1(),( xyxyxf

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Deep Feedforward Neural Networks

• 2-stage training process: i) unsupervised pre-training; ii) fine tuning (vanishing gradients problem is beaten!).

• Number of hidden layers > 1 (usually 6-9).• 100K – 100M free parameters.• No (or less) feature preprocessing stage. 17 /

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Sparse Autoencoders

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Dimensionality reduction

• Use a stacked RBM as deep auto-encoder

1. Train RBM with images as input & output

2. Limit one layer to few dimensions

Information has to pass through middle layer

G. E. Hinton and R. R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks // Science 313 (2006), p. 504 – 507.19 /

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Original

Deep RBN

PCA


Olivetti face data, 25x25 pixel images reconstructed from 30 dimensions (625 30)


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How to use unsupervised pre-training stage / 1

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Unlabeled data

Unlabeled data is readily available

Example: Images from the web

1. Download 10’000’000 images2. Train a 9-layer DNN3. Concepts are formed by DNN


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PCA Deep RBN

804’414 Reuters news stories, reduction to 2 dimensions


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Hierarchy of trained representations

Low-level feature

Middle-level

feature

Top-level feature

Feature visualization of convolutional net trained on ImageNet from [Zeiler & Fergus 2013]

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Hessian-Free optimization: Deep Learning with no pre-training stage

J. Martens. Deep Learning via Hessian-free Optimization // Proceedings of the 27th International Conference on Machine Learning (ICML), 2010. 28 /

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FLOPS comparison

https://ru.wikipedia.org/wiki/FLOPS

Type Name Flops Cost

Mobile Raspberry Pi 1st Gen, 700 Mhz

0,04 Gflops $35

Mobile Apple A8 1,4 Gflops $700 (in iPhone 6)

CPU Intel Core i7-4930K (Ivy Bridge), 3.7 GHz

140 Gflops $700

CPU Intel Core i7-5960X (Haswell), 3.0 GHz

350 Gflops $1300

GPU NVidia GTX 980 4612 Gflops (single precision),

144 Gflops (double precision)

$600 + cost of PC (~$1000)

GPU NVidia Tesla K80 8740 Gflops (single precision),

2910 Gflops (double precision)

$4500 + cost of PC (~1500)

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Deep Networks Training time using GPU

• Pretraining – from 2-3 weeks to 2-3 months.

• Fine-tuning (final supervised training) – from 1 day to 1 week.

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Tools for training Deep Neural Networks

D. Kruchinin, E. Dolotov, K. Kornyakov, V. Kustikova, P. Druzhkov. The Comparison of Deep Learning Libraries on the Problem of Handwritten Digit Classication // Analysis of Images, Social Networks and Texts (AIST), 2015, April, 9-11th, Yekaterinburg. 31 /

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Convolutional Neural Networks: Return of Jedi

Andrej Karpathy and Fei-Fei. CS231n: Convolutional Neural Networks for Visual Recognition http://cs231n.github.io/convolutional-networks

Yoshua Bengio, Ian Goodfellow and Aaron Courville. Deep Learning // An MIT Press book in preparation http://www-labs.iro.umontreal.ca/~bengioy/DLbook

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http://cs231n.github.io/convolutional-networks



http://www-labs.iro.umontreal.ca/~bengioy/DLbook/


AlexNet, CNN-Mega-HiT, results on LSVRC-2012

A. Kryzhevsky, I. Sutskever, G.E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks // Advances in Neural Information Processing Systems 25 (NIPS 2012).

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Lazy Deep Learning: idea

A. S. Razavian, H. Azizpour, J. Sullivan, S. Carlsson. CNN Features off-the-shelf: an Astounding Baseline for Recognition //2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 23-28 June 2014, Columbus, USA, p. 512 – 519. 34 /

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Lazy Deep Learning: bechmark results

A. S. Razavian, H. Azizpour, J. Sullivan, S. Carlsson. CNN Features off-the-shelf: an Astounding Baseline for Recognition //2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 23-28 June 2014, Columbus, USA, p. 512 – 519. 35 /

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MIT-8 toy problem: formulation

• 8 classes• 2080 images in

total• TRAIN: 2000

images (250 per class)

• TEST: 688 images, 86 per class

S. Banerji, A. Verma, C. Liu. Novel Color LBP Descriptors for Scene and ImageTexture Classification // Cross Disciplinary Biometric Systems, 2012, 15th International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, Nevada, pp. 205-225.

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MIT-8 toy problem: results

Acc.TRAIN

Acc.TEST

1 LBP + SVM with RBF Kernel 27,2% 19,0%

2 LPQ + SVM with RBF kernel 38,4% 30,5%

3 LBP + SVM with χ2 kernel 94,2% 74,0%

4 LPQ + SVM with χ2 kernel 99,1% 82,2%

5 Deep CNN (AlexNet) + SVM RBF kernel (LAZY DL)

95,1% 91,8%

6 Deep CNN (AlexNet) + SVM with χ2 Kernel (LAZY DL)

100,0% 93,2%

7 Deep CNN (AlexNet) + MLP (LAZY DL) 100,0% 92,3%Original results, to be published. 37 /

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ZZ Photo – photo organizer

Trial version is available at http://zzphoto.me38 /

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http://zzphoto.me/

Viola-Jones Object Detector

• Very popular for Human Face Detection.• May be trained for Cat and Dog Face detection.• Available free in OpenCV library (http://opencv.org).

O. Parkhi, A. Vedaldi, C. V. Jawahar, and A. Zisserman. The Truth about Cats and Dogs // Proceedings of the International Conference on Computer Vision (ICCV), 2011. J. Liu, A. Kanazawa, D. Jacobs, P. Belhumeur. Dog Breed Classification Using Part Localization // Lecture Notes in Computer Science Volume 7572, 2012, pp 172-185.

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http://opencv.org/

Images pyramid for Viola-Jones

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Viola-Jones Object Detector Classifier Structure

P. Viola, M. Jones. Rapid object detection using a boosted cascade of simple features // Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001.

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AlexNet design

A. Kryzhevsky, I. Sutskever, G.E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks // Advances in Neural Information Processing Systems 25 (NIPS 2012).

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Pets detection problem (Kaggle Dataset + random Other images)

• Kaggle Dataset + random “other” images;

• 2 classes (cats & dogs VS other);

• TRAIN: 5,000 samples;

• TEST: 12,000 samples. 43 /

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Pets detection results: FAR vs FRR graphs

Original results, to be published. 44 / 62

Pet detection results : ROC curve


Pets detection results, FAR error is fixed to 0.5%

FRR Error

1 Viola-Jones Face Detector for Cats & Dogs + LBP + SVM

79,73%

2 AlexNet, argmax (STANDARD DL, ImageNet-2012, 1000)

32,05%

3 AlexNet, sum (STANDARD DL, ImageNet-2012, 1000)

26,11%

4 AlexNet + SVM linear (LAZY DL) 4,35%


Development of AleksNet on OpenCV

VGG MatConvNet: CNNs for MATLAB http://www.vlfeat.org/matconvnet/mexopencv:MATLAB-OpenCV interface http://kyamagu.github.io/mexopencv/matlab

MatConvNet, MATLAB +

CUDA

OpenCV app, C++

YAML

YAML, BIN

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http://www.vlfeat.org/matconvnet/



http://kyamagu.github.io/mexopencv/matlab

http://kyamagu.github.io/mexopencv/matlab

Convolution Layer



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Pooling layer



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Activation functions



𝑓 (𝑥)=max (0 ,𝑥 )

𝑓 ′ (𝑥 )={1 ,𝑥 ≥00 ,𝑥<0

ReLU activation function

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Implementation tricks: im2col

K. Chellapilla, S. Puri, P. Simard. High Performance Convolutional Neural Networks for Document Processing // International Workshop on Frontiers in Handwriting Recognition, 2006.

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Implementation tricks: im2col for convolution

K. Chellapilla, S. Puri, P. Simard. High Performance Convolutional Neural Networks for Document Processing // International Workshop on Frontiers in Handwriting Recognition, 2006.

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Matrix multiplicationMatrices’ size C OpenCV

C++ (use STL vector

class)OpenBLAS Matlab

1000×1000 1.45 1.76 1.47 0.062 0.062

2000×2000 11.64 14.2 11.23 0.99 0.54

3000×3000 38.11 47.2 37.99 1.75 1.7

4000×4000 90.84 110.37 90.2 7.91 4.2

5000×5000 180.74 213.4 181.02 10.8 7.3

6000×6000 315.46 376.46 316.3 25.33 12.74

https://4fire.wordpress.com/2012/04/29/matrices-multiplication-on-windows-matlab-is-the-champion-again/

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OpenBLAS

• OpenBLAS is an open source implementation of the BLAS (Basic Linear Algebra Subprograms) API with many hand-crafted optimizations for specific processor types.

http://www.openblas.net/54 /

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https://en.wikipedia.org/wiki/BLAS

http://www.openblas.net/

http://www.openblas.net/

Sizes of layers

LAYER 1-4 LAYER 5-8 LAYER 9-10 LAYER 11-12

LAYER 13-15

LAYER 16-17

LAYER 18-19

LAYER 20-21

0.09 1.56 2.25 2.25 2.25

144.02

64.02

15.63

Siz

e,

mb

~ 8,5 mb ~ 223 mb

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Pets test #2: data

1 mini-set:- 500 cats- 500 dogs- 1000 negatives

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Pets test #2: results

100 200 500 1000 2000 5000 10000 180000.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

15 layer17 layer19 layer

Train size

FR

R,

%

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Pets test #2: results - FRR, % (FAR is fixed to 0,5%)

Layer #

Train size 15 16 19

100 30,08 12,61 12,94

500 17,91 10,41 10,72

1000 11,59 7,52 6,80

5000 7,41 3,88 4,13

10000 6,29 3,66 2,71

18000 5,16 2,64 2,54

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Calculation speed

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200

5

10

15

20

25

30

35

40

45

Layer #

Tim

e,

ms

~ 73 ms ~ 60 ms59 /

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Labeled Faces in the Wild (LFW) Dataset

G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments // University of Massachusetts, Amherst, Technical Report 07-49, October, 2007

• more than 13,000 images of faces collected from the web.

• Pairs comparison, restricted mode.

• test: 10-fold cross-validation, 6000 face pairs.

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Face Recognition on LWF, results

Y. Taigman, M. Yang, M. Ranzato, L. Wolf. DeepFace: Closing the Gap to Human-Level Performance in Face Verification, 2014, CVPR.

Accuracy, %

1 Principal Component Analysis (EigenFaces) 60,2%

2 Local Binary Pattern Histograms (LBP) 72,4%

3 Deep CNN (AlexNet) + Euclid (LAZY DL) 71,0%

4 DeepFace by Facebook (STANDARD DL) 97,25%

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contact: [email protected]@gmail.com

Thanks!

mailto:[email protected]

mailto:[email protected]

Software

Details of Lazy Deep Learning for Images Recognition in ZZ Photo app