Faire de la reconnaissance d'images avec le Deep Learning - Cristina & Pierre @ Photobox
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The document discusses the fundamentals of deep learning, particularly through the use of Convolutional Neural Networks (CNNs) for image processing and classification. It outlines key components such as convolutional layers, activation functions, and pooling techniques, and emphasizes the importance of feature extraction and dimensionality reduction. The content also addresses practical applications of CNNs, techniques for dataset creation, and the learning process involving backpropagation.
![Layers used to build ConvNets
INPUT → will hold the raw pixel values of the image (e.g.[32,32,3] - image with width 32,
height 32 and 3 channels: RGB)
Convolutional layer (CONV) → compute the output of neurons connected to
local regions in the input (e.g.: output of size [32x32x12] if we decided to use 12 filters)
RELU → apply an elementwise activation function, such as the max(0,x) thresholding at
zero. (e.g. the size of the volume is unchanged [32x32x12])
POOL → perform a downsampling operation along the spatial dimensions (width, height),
resulting in volume such as [16x16x12].
Fully connected layer (FC) → compute the class scores, and it connects all
neurons in this layer with all the neurons in the previous one. (e.g. the size is [1, 1, 10] if we
have 10 classes)](https://image.slidesharecdn.com/jedhaxphotobox-imagerecognitionwithdeeplearning-190425170000/85/Faire-de-la-reconnaissance-d-images-avec-le-Deep-Learning-Cristina-Pierre-Photobox-12-320.jpg)
Faire de la reconnaissance d'images avec le Deep Learning - Cristina & Pierre @ Photobox
- 1. Data Science Bootcamp Commencez votre carrière dans la Data
- 2. Nos Speakers — Cristina Oprean & Pierre Stefani Machine Learning Engineer
- 3. The Rise of Deep Learning Healthcare Autonomous vehicles Reinforcement learning Robotics Language Processing GANs
- 4. What is Deep Learning ?
- 5. Why Deep Learning ? Hand engineered features are time consuming, brittle and not scalable in practice Can we learn the underlying features directly from data?
- 6. Why now ? Neural Networks date back decades, so why the resurgence?
- 7. Inspired from neuroscience - Signal Input - Neuron activation Decision
- 8. Fully connected networks Fully connected: Connect neuron in hidden layer to all neurons in input layer No spatial information! And many, many parameters! Input: 2D image Vector of pixel values
- 9. Using spatial structure Idea: connect patches of input to neurons in the hidden layer. Neuron connected to region of input. Only “sees” these values Input: 2D image. Array of pixel values
- 10. Applying Filters to Extract Features 1) Apply a set of weights – a filter – to extract local features 2) Use multiple filters to extract different features 3) Spatially share parameters of each filter
- 12. Layers used to build ConvNets INPUT → will hold the raw pixel values of the image (e.g.[32,32,3] - image with width 32, height 32 and 3 channels: RGB) Convolutional layer (CONV) → compute the output of neurons connected to local regions in the input (e.g.: output of size [32x32x12] if we decided to use 12 filters) RELU → apply an elementwise activation function, such as the max(0,x) thresholding at zero. (e.g. the size of the volume is unchanged [32x32x12]) POOL → perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12]. Fully connected layer (FC) → compute the class scores, and it connects all neurons in this layer with all the neurons in the previous one. (e.g. the size is [1, 1, 10] if we have 10 classes)
- 13. Input layer Existing color spaces for images: RBG, Grayscale, HSV, Lab, etc. CNNs - architectures specific for images Reduce the images into a form easier to process Without loosing features needed for a good recognition
- 14. Convolutional layer (CONV) Spatial arrangement: Depths (D) → nb of filters we want to use Stride (S) → value with which we slide the filter Padding (P) → the number of zeros we add to the border Input size (I) → size of the image Filter size (F) → size of the convolution => Size of the output: (I−F+2P)/S+1 E.g. i=5, F=3, P=0, S=1 => O=(5-3+0)/1+1=3
- 15. Convolutional layer (CONV) Conv layers: In CNNs architectures can have multiple Conv layers 1st layer captures low-level features Final layers capture high-level features When the image has 3 channels (RGB) => the filter has D = 3
- 16. Pooling layer (POOL) Reduces the spatial size of the convolved feature Decreases computational power Extracts dominant features Has no parameters to learn Two types: max and average pooling
- 17. Pooling layer (POOL) Max pooling performs better than average pooling Max pooling is a noise suppressant
- 18. RELU (activation function) Introduces non-linearities into the network
- 19. RELU (activation function) Linear Activation functions produce linear decisions no matter the network size Non-linearities allow us to approximate arbitrarily complex functions
- 20. CNNs for feature learning 1. Learn features in input image through convolution 2. Introduce non-linearity through activation function (real-world data is non-linear!) 3. Reduce dimensionality and preserve spatial invariance with pooling
- 21. CNNs for classification 1. CONV and POOL layers output high-level features of input 2. Fully connected layer uses these features for classifying input image 3. Express output as probability of image belonging to a particular class
- 22. CNNs: training with backpropagation Learn weights for convolutional filters and fully connected layers
- 24. CNNs: Tips - dataset creation Where to find datasets https://toolbox.google.com/datasetsearch https://www.kaggle.com/datasets Crawl the web ! (Flickr, Bing, Google) Watch out for scientific paper releases and conference workshops
- 26. CNNs: Tips - training ● No need to start from scratch (train with less data, start from pretrained models, domain adaptation) ● Monitor loss and validation accuracies ● Optimize once it works Batch size Learning Rates Modules & Loss
- 27. Some applications @Photobox and not only
- 28. Photo crop: naive version ⇒ How to crop the photo and keep the main subject ?
- 29. Photo crop: using face detection ⇒ Face detection allows to have a crop focused on the faces
- 30. Near duplicates filtering Without duplicates filtering With duplicates filtering
- 31. Near duplicates filtering 0.4 0 0.1 … 0 Remove Feature comparison allows us to find near duplicates and remove them Goal : reduce the hassle of duplicate selection
- 32. Near duplicates filtering Goal : reduce the hassle of duplicate selection Feature comparison allows us to find near duplicates and remove them 0.4 5 -3 … 3 Remove
- 33. Near duplicates filtering -4 15 20 … 10 Remove Feature comparison allows us to find near duplicates and remove them Goal : reduce the hassle of duplicate selection
- 34. Feature comparison allows us to find near duplicates and remove them -40 5 10 … 11 Remove Near duplicates filtering Goal : reduce the hassle of duplicate selection
- 35. Feature comparison allows us to find near duplicates and remove them 102 -32 120 … 33 Keep Near duplicates filtering Goal : reduce the hassle of duplicate selection
- 36. Photo selection with aesthetics ● Propose book covers or photo candidates for full page photos in a book ● Suggest premium products with the most beautiful pictures from a set
- 37. Photo selection with aesthetics ● Select the most beautiful photo from a set of duplicates 0.73 0.52 0.53 0.67
- 39. Let’s pratice !
- 40. How it ‘learns’: minimizing the error input expected output expected output output ‘ cat ‘ Error (ou loss)
- 41. How it ‘learns’ : minimizing the error minimum Starting point
- 42. How it ‘learns’: minimizing the error minimum Starting point
- 43. How it ‘learns’: backpropagation input expected output output Error (ou loss)
- 44. Takeaways No algorithm is 100% accurate It depends on: How much data we have The quality of the data Performance tradeoff Hardware Some algorithms are better than humans, some of them are way behind
- 47. How it ‘learns’ : minimizing the error - Define a loss function - Minimize this loss - Gradient descent algorithm