deep learning for computer vision - executiveml
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Deep Learning for Computer VisionExecutive-ML 2017/09/21
Neither Proprietary nor Confidential – Please Distribute ;)
Alex Conway
alex @ numberboost.com@alxcnwy
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Hands up!
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Check out theDeep Learning Indaba
videos & practicals!
http://www.deeplearningindaba.com/videos.htmlhttp://www.deeplearningindaba.com/practicals.html
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Deep Learning is Sexy (for a reason!)
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Image Classification
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http://yann.lecun.com/exdb/mnist/
https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py(99.25% test accuracy in 192 seconds and 70 lines of code)
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Image Classification
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Image Classification
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https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
Object detection
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https://www.youtube.com/watch?v=VOC3huqHrss
Object detection
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Image Captioning & Visual Attention
XXX
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https://einstein.ai/research/knowing-when-to-look-adaptive-attention-via-a-visual-sentinel-for-image-captioning
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Image Q&A
11https://arxiv.org/pdf/1612.00837.pdf
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Video Q&A
XXX
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https://www.youtube.com/watch?v=UeheTiBJ0Io
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Pix2Pix
https://affinelayer.com/pix2pix/https://github.com/affinelayer/pix2pix-tensorflow
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Pix2Pix
https://medium.com/towards-data-science/face2face-a-pix2pix-demo-that-mimics-the-facial-expression-of-the-german-chancellor-b6771d65bf66
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Original inputRear Window (1954)
Pix2pix outputFully Automated
RemasteredPainstakingly by Hand
https://hackernoon.com/remastering-classic-films-in-tensorflow-with-pix2pix-f4d551fa0503
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Style Transfer
https://github.com/junyanz/CycleGAN16
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Style Transfer MAGIC
https://github.com/junyanz/CycleGAN17
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1. What is a neural network?
2. What is a convolutional neural network?
3. How to use a convolutional neural network
4. More advanced Methods
5. Case studies & applications
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Big Shout Outs
Jeremy Howard & Rachel Thomashttp://course.fast.ai
Andrej Karpathyhttp://cs231n.github.io
François Chollet (Keras lead dev)https://keras.io/
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1.What is a neural network?
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What is a neuron?
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• 3 inputs [x1,x2,x3] • 3 weights [w1,w2,w3]• Element-wise multiply and sum • Apply activation function f• Often add a bias too (weight of 1) – not shown
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What is an Activation Function?
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Sigmoid Tanh ReLU
Nonlinearities … “squashing functions” … transform neuron’s outputNB: sigmoid output in [0,1]
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What is a (Deep) Neural Network?
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Inputs outputs
hiddenlayer 1
hiddenlayer 2
hiddenlayer 3
Outputs of one layer are inputs into the next layer
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How does a neural network learn?
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• You need labelled examples “training data”
• Initially, the network makes random predictions (weights initialized randomly)
• For each training data point, we calculate the error between the network’s predictions and the ground-truth labels (aka “loss function”)
• Use ‘backpropagation’ (really just the chain rule), to update the network parameters (weights) in the opposite direction to the error
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How does a neural network learn?
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New weight = Old
weightLearning
rate-Gradient of weight with
respect to Error( )x“How much
error increaseswhen we increase
this weight”
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Gradient Descent Interpretation
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http://scs.ryerson.ca/~aharley/neural-networks/
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http://playground.tensorflow.org
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What is a Neural Network?
For much more detail, see:
1. Michael Nielson’s Neural Networks & Deep Learning free online book http://neuralnetworksanddeeplearning.com/chap1.html
2. Anrej Karpathy’s CS231n Noteshttp://neuralnetworksanddeeplearning.com/chap1.html
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2. What is a convolutional
neural network?
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What is a Convolutional Neural Network?
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“like a ordinary neural network but with special types of layers that work well on images”
(math works on numbers)
• Pixel = 3 colour channels (R, G, B)
• Pixel intensity ∈[0,255]• Image has width w and height h• Therefore image is w x h x 3 numbers
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31This is VGGNet – don’t panic, we’ll break it down piece by piece
Example Architecture
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32This is VGGNet – don’t panic, we’ll break it down piece by piece
Example Architecture
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Convolutions
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http://setosa.io/ev/image-kernels/
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Convolutions
34http://deeplearning.net/software/theano/tutorial/conv_arithmetic.html
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New Layer Type: Convolutional Layer
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• 2-d weighted average when multiply kernel over pixel patches• We slide the kernel over all pixels of the image (handle borders)• Kernel starts off with “random” values and network updates (learns)
the kernel values (using backpropagation) to try minimize loss• Kernels shared across the whole image (parameter sharing)
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Many Kernels = Many “Activation Maps” = Volume
36http://cs231n.github.io/convolutional-networks/
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New Layer Type: Convolutional Layer
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Convolutions
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https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py
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Convolutions
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Convolutions
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Convolutions
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Convolution Learn Heirarchial Features
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Great vid
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https://www.youtube.com/watch?v=AgkfIQ4IGaM
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New Layer Type: Max Pooling
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New Layer Type: Max Pooling
• Reduces dimensionality from one layer to next• …by replacing NxN sub-area with max value• Makes network “look” at larger areas of the image at a time• e.g. Instead of identifying fur, identify cat• Reduces overfitting since losing information helps the network generalize
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New Layer Type: Max Pooling
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Softmax
• Convert scores∈ℝ to probabilities∈ [0,1]
• Then predict the class with highest probability
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Bringing it all together
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Convolution + max pooling + fully connected + softmax
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Bringing it all together
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Convolution + max pooling + fully connected + softmax
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Bringing it all together
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Convolution + max pooling + fully connected + softmax
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Bringing it all together
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Convolution + max pooling + fully connected + softmax
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Bringing it all together
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Convolution + max pooling + fully connected + softmax
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Bringing it all together
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Convolution + max pooling + fully connected + softmax
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We need labelled training data!
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ImageNet
55http://image-net.org/explore
1000 object categories
1.2 million training images
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ImageNet
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ImageNet
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ImageNet Top 5 Error Rate
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TraditionalImage Processing Methods
AlexNet8 Layers
ZFNet8 Layers
GoogLeNet22 Layers ResNet
152 Layers SENetEnsamble
TSNetEnsamble
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3. How to use a convolutional neural
network
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Using a Pre-Trained ImageNet-Winning CNN
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• We’ve been looking at “VGGNet”
• Oxford Visual Geometry Group (VGG)
• ImageNet 2014 Runner-up
• Network is 16 layers (deep!)
• Easy to fine-tune
https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
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Example: Classifying Product Images
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https://github.com/alexcnwy/CTDL_CNN_TALK_20170620
Classifying
products into
9 categories
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62https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
Start with Pre-Trained ImageNet Model
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“Transfer Learning”is a game changer
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Fine-tuning A CNN To Solve A New Problem• Cut off last layer of pre-trained Imagenet winning CNN• Keep learned network (convolutions) but replace final layer• Can learn to predict new (completely different) classes• Fine-tuning is re-training new final layer - learn for new task
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Fine-tuning A CNN To Solve A New Problem
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Before Fine-Tuning
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After Fine-Tuning
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Fine-tuning A CNN To Solve A New Problem
• Fix weights in convolutional layers (set trainable=False)
• Remove final dense layer that predicts 1000 ImageNet classes
• Replace with new dense layer to predict 9 categories
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88% accuracy in under 2 minutes for classifying products into categories
Fine-tuning is awesome!Insert obligatory brain analogy
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Visual Similarity
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• Chop off last 2 VGG layers
• Use dense layer with 4096 activations
• Compute nearest neighbours in the space of these activations
https://memeburn.com/2017/06/spree-image-search/
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https://github.com/alexcnwy/CTDL_CNN_TALK_20170620
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Input Imagenot seen by model
ResultsTop 10 most “visually similar”
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4. More Advanced Methods
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Use a Better Architecture (or all of them!)
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“Ensambles win”
learn a weighted average of many models’ predictions
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cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf
There are MANY Computer Vision Tasks
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> Long, Shelhamer, and Darrell, “Fully Convolutional Networks for Semantic Segmentation” CVPR 2015
> Noh et al, “Learning Deconvolution Network for Semantic Segmentation” CVPR 2015
Semantic Segmentation
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http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf
Object detection
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Object detection
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https://www.youtube.com/watch?v=VOC3huqHrss
Object detection
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http://blog.romanofoti.com/style_transfer/https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py
Style TransferLoss = content_loss + style_loss
Content loss = convolutions from pre-trained networkStyle loss = gram matrix from style image convolutions
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Video Q&A
XXX
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https://www.youtube.com/watch?v=UeheTiBJ0Io
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Video Q&A
XXX
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https://www.youtube.com/watch?v=UeheTiBJ0Io
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5. Case Studies
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Image & Video Moderation
TODO
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Large international gay dating app with tens of millions of users uploading hundreds-of-thousands of photos per day
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Estimating Accident Repair Cost from Photos
TODO
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Prototype for large SA insurer
Detect car make & model from registration disk
Predict repair cost using learned model
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Optical Sorting
TODO
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https://www.youtube.com/watch?v=Xf7jaxwnyso
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Segmenting Medical Images
TODO
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m
Counting People
TODO
Count shoppers, segment on age & genderfacial recognition loyalty is next
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Counting Cars
TODO
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Wine A.I.
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Detecting Potholes
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GET IN TOUCH!
Alex Conway
alex @ numberboost.com@alxcnwy
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http://blog.kaggle.com/2016/02/04/noaa-right-whale-recognition-winners-interview-2nd-place-felix-lau/
Computer Vision Pipelines
https://flyyufelix.github.io/2017/04/16/kaggle-nature-conservancy.html
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https://www.autoblog.com/2017/08/04/self-driving-car-sign-hack-stickers/
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Practical Tips• use a GPU – AWS p2 instances (use spot!)
• when overfitting (validation_accuracy <<< training_accuracy)– Increase dropout – Early stopping
• when underfitting (low training_accuracy)1. Add more data2. Use data augmentation
– Flipping / stretching / rotating– Slightly change hues / brightness
3. Use more complex model– Resnet / Inception / Densenet– Ensamble (average <<< weighted average with learned weights)
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