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🍪Chapter 14. Deep Computer Vision Using Convolutional Neural Networks
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Chapter 14. Deep Computer Vision Using Convolutional Neural Networks
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Chapter 14. Deep Computer Vision Using Convolutional Neural Networks
This is a short companion page to our internal reading group of the book “Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition”. However I unashamedly used a lot of PyTorch examples.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Chapter 14. Deep Computer Vision Using Convolutional Neural Networks Although IBM's Deep Blue supercomputer beat the chess world champion Garry Kasparov back in 1996, it wasn't until fairly recently that ... - Selection from Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition [Book]
https://learning.oreilly.com/library/view/hands-on-machine-learning/9781492032632/ch14.html

Intuition: visual cortex

Week 3 - Lecture: Convolutional neural networks
Course website: http://bit.ly/pDL-homePlaylist: http://bit.ly/pDL-YouTubeSpeaker: Yann LeCunWeek 3: http://bit.ly/pDL-en-030:00:00 - Week 3 - LectureLECTURE ...
https://youtu.be/FW5gFiJb-ig?t=2616

Convolution

Convolution - Wikipedia
In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions (f and g) that produces a third function ( convolution refers to both the result function and to the process of computing it. It is defined as the ) that expresses how the shape of one is modified by the other.
https://en.wikipedia.org/wiki/Convolution

Filters, kernels, padding, strides, and pooling layers

CNN Explainer
An interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs).
https://poloclub.github.io/cnn-explainer/

Putting it altogether: LeNet5

Week 3 - Lecture: Convolutional neural networks
Course website: http://bit.ly/pDL-homePlaylist: http://bit.ly/pDL-YouTubeSpeaker: Yann LeCunWeek 3: http://bit.ly/pDL-en-030:00:00 - Week 3 - LectureLECTURE ...
https://youtu.be/FW5gFiJb-ig

CNN as a strong prior: locality, stationarity, compositionality

ConvNet Evolutions, Architectures, Implementation Details and Advantages.
🎙️ Yann LeCun Inspired by Fukushima's work on visual cortex modelling, using the simple/complex cell hierarchy combined with supervised training and backpropagation lead to the development of the first CNN at University of Toronto in '88-'89 by Prof. Yann LeCun. The experiments used a small dataset of 320 'mouser-written' digits.
https://atcold.github.io/pytorch-Deep-Learning/en/week03/03-2/

Residue block and skip connections

An Overview of ResNet and its Variants
As ResNet gains more and more popularity in the research community, its architecture is getting studied heavily. In this section, I will first introduce several new architectures based on ResNet, then introduce a paper that provides an interpretation of treating ResNet as an ensemble of many smaller networks. Xie et al.
https://towardsdatascience.com/an-overview-of-resnet-and-its-variants-5281e2f56035

Deep CNN model architectures

AlexNet

GoogLeNet

ResNet 50

Other models

torchvision.models - Torchvision 0.10.0 documentation
The models subpackage contains definitions of models for addressing different tasks, including: image classification, pixelwise semantic segmentation, object detection, instance segmentation, person keypoint detection and video classification. The models subpackage contains definitions for the following model architectures for image classification: You can construct a model with random weights by calling its constructor: We provide pre-trained models, using the PyTorch .
https://pytorch.org/vision/stable/models.html

Classification, detection, and semantic segmentation

Detection and Segmentation through ConvNets
There are wide variety of applications of neural networks in the realm of computer vision. And with a bit of twist same tools and techniques can be applied across wide range of tasks effectively. In this article we'll walk through few of those applications and way to approach towards them.
https://towardsdatascience.com/detection-and-segmentation-through-convnets-47aa42de27ea

IoU and NMS

Non-maximum Suppression (NMS)
Typical Object detection pipeline has one component for generating proposals for classification. Proposals are nothing but the candidate regions for the object of interest. Most of the approaches employ a sliding window over the feature map and assigns foreground/background scores depending on the features computed in that window.
https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c

R-CNN, Feature Pyramid Network (FPN), Region Proposal Networks (RPN)

R-CNN, Fast R-CNN, Faster R-CNN, YOLO - Object Detection Algorithms
Computer vision is an interdisciplinary field that has been gaining huge amounts of traction in the ecent years(since CNN) and self-driving cars have taken centre stage. Another integral part of computer vision is object detection. Object detection aids in pose estimation, vehicle detection, surveillance etc.
https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e

Mask RCNN

GitHub - matterport/Mask_RCNN: Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. The repository includes: Source code of Mask R-CNN built on FPN and ResNet101.
https://github.com/matterport/Mask_RCNN

Towards realtime, embedded: NAS and MobileNets

MobileNets: Open-Source Models for Efficient On-Device Vision
Deep learning has fueled tremendous progress in the field of computer vision in recent years, with neural networks repeatedly pushing the frontier of visual recognition technology.
https://ai.googleblog.com/2017/06/mobilenets-open-source-models-for.html

Towards low prior: vision without CNN

Transformers for Image Recognition at Scale
While convolutional neural networks (CNNs) have been used in computer vision since the 1980s, they were not at the forefront until 2012 when AlexNet surpassed the performance of contemporary state-of-the-art image recognition methods by a large margin.
https://ai.googleblog.com/2020/12/transformers-for-image-recognition-at.html

Towards low data: contrastive representation learning, zero shots or few shots

CLIP: Connecting Text and Images
We're introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the "zero-shot" capabilities of GPT-2 and GPT-3.
https://openai.com/blog/clip/

Towards explainability

Feature Visualization
There is a growing sense that neural networks need to be interpretable to humans. The field of neural network interpretability has formed in response to these concerns. As it matures, two major threads of research have begun to coalesce: feature visualization and attribution. This article focuses on feature visualization.
https://distill.pub/2017/feature-visualization/
Multimodal Neurons in Artificial Neural Networks
We are deeply grateful to Sandhini Agarwal, Daniela Amodei, Dario Amodei, Tom Brown, Jeff Clune, Steve Dowling, Gretchen Krueger, Brice Menard, Reiichiro Nakano, Aditya Ramesh, Pranav Shyam, Ilya Sutskever and Martin Wattenberg. Gabriel Goh: Research lead.
https://distill.pub/2021/multimodal-neurons/
 
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July 27, 2021
 
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