"Designing CNN Algorithms for Real-time Applications," a Presentation from Almond AI
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For the full video of this presentation, please visit: https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-chiu For more information about embedded vision, please visit: http://www.embedded-vision.com Matthew Chiu, Founder of Almond AI, presents the "Designing CNN Algorithms for Real-time Applications" tutorial at the May 2017 Embedded Vision Summit. The real-time performance of CNN-based applications can be improved several-fold by making smart decisions at each step of the design process – from the selection of the machine learning framework and libraries used to the design of the neural network algorithm to the implementation of the algorithm on the target platform. This talk delves into how to evaluate the runtime performance of a CNN from a software architecture standpoint. It then explains in detail how to build a neural network from the ground up based on the requirements of the target hardware platform. Chiu shares his ideas on how to improve performance without sacrificing accuracy, by applying recent research on training very deep networks. He also shows examples of how network optimization can be achieved at the algorithm design level by making a more efficient use of weights before the model is compressed via more traditional methods for deployment in a real-time application.
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Single-photon avalanche diodes (SPADs) are novel sensors that can detect individual photons with high time resolution. SPADs allow for imaging with extreme dynamic range from low to high light conditions without saturation. They also enable minimal motion blur imaging due to their ability to precisely timestamp single photons. Recent research has demonstrated burst photography using SPAD arrays that can reconstruct non-rigid scene motion and produce almost motion-blur free images in dark environments. However, challenges remain in increasing resolution, reducing data rates and power consumption before widespread commercial applications can be realized.“Case Study: Facial Detection and Recognition for Always-On Applications,” a ...
“Case Study: Facial Detection and Recognition for Always-On Applications,” a ...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/case-study-facial-detection-and-recognition-for-always-on-applications-a-presentation-from-synopsys/
Jamie Campbell, Product Marketing Manager for Embedded Vision IP at Synopsys, presents the “Case Study: Facial Detection and Recognition for Always-On Applications” tutorial at the May 2021 Embedded Vision Summit.
Although there are many applications for low-power facial recognition in edge devices, perhaps the most challenging to design are always-on, battery-powered systems that use facial recognition for access control. Laptop, tablet and cellphone users expect hands-free and instantaneous facial recognition. This means the electronics must be always on, constantly looking to detect a face, and then ready to pull from a data set to recognize the face.
This presentation describes the challenges of moving traditional facial detection neural networks to the edge. It explores a case study of a face recognition access control application requiring continuous operation and extreme energy efficiency. Finally, it describes how the combination of Synopsys DesignWare ARC EM and EV processors provides low-power, efficient DSP and CNN acceleration for this application."Quantizing Deep Networks for Efficient Inference at the Edge," a Presentatio...
"Quantizing Deep Networks for Efficient Inference at the Edge," a Presentatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.Enabling Power-Efficient AI Through Quantization
Artificial Intelligence (AI), specifically deep learning, is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, the deep neural networks of today use too much memory, compute, and energy. Plus, to make AI truly ubiquitous, networks need to run on the end device within a tight power and thermal budget. One approach to help address these issues is quantization, which attempts to reduce the number of bits used for weight parameters and activation calculations without sacrificing model accuracy. This presentation covers: why quantization is important, existing quantization challenges, Qualcomm AI Research's existing quantization research, and how developers and researchers can take advantage of quantization on Qualcomm Snapdragon.
“Efficient Deep Learning for 3D Point Cloud Understanding,” a Presentation fr...
“Efficient Deep Learning for 3D Point Cloud Understanding,” a Presentation fr...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.AI+ Remote Sensing: Applying Deep Learning to Image Enhancement, Analytics, a...
AI+ Remote Sensing: Applying Deep Learning to Image Enhancement, Analytics, a...Jui-Hsin (Larry) Lai
A Keynote Speech in DigitalWorld Congress 2021.
Youtube Video https://www.youtube.com/watch?v=FdUyaUJTYLY
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group. 20211118 AI+ Remote Sensing
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group.
Joint super resolution and denoising from a single depth image
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
MobileNet Review | Mobile Net Research Paper Review | MobileNet v1 Paper Expl...
Hi, In this lesson I will discuss how you can read a research paper and I will explain the MobileNet research paper published in 2017. I will first show you paper and the will present key findings through a .PPT presentation. I hope you would find it useful and like this video.
Learn Complete Data Science with these 5 video series.
1. Python for Beginners
https://www.youtube.com/watch?v=b42eTWkEIfA&list=PLc2rvfiptPSRmd4eWpRmzRIPebX3W9mju
2. Machine Learning for Beginners
https://www.youtube.com/watch?v=ZeM2tHtjGy4&list=PLc2rvfiptPSTvPFbNlT_TGRupzKKhJSIv
3. Feature Selection in Machine Learning
https://www.youtube.com/watch?v=kA4mD3y4aqA&list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH
4. Deep Learning with TensorFlow 2.0 and Keras
https://www.youtube.com/watch?v=nVvhkVLh60o&list=PLc2rvfiptPSR3iwFp1VHVJFK4yAMo0wuF
5. Natural Language Processing (NLP) Tutorials
https://www.youtube.com/watch?v=mrF9MD56-wk&list=PLc2rvfiptPSQgsORc7iuv7UxhbRJox-pW&index=1
The working code is given in the video description of each video. You can download the Jupyter notebook from GitHub.
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Make Your Own Automated Email Marketing Software in Python
https://www.youtube.com/watch?v=gmYuom6kfoY&list=PLc2rvfiptPSQK9ErKaLqf40iu1A3le9Zr
“High-fidelity Conversion of Floating-point Networks for Low-precision Infere...
“High-fidelity Conversion of Floating-point Networks for Low-precision Infere...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.Small Deep-Neural-Networks: Their Advantages and Their Design
This document discusses small deep neural networks, their advantages, and their design. It notes that computer vision tasks now work well due to advances in deep learning. Small neural networks have advantages for applications requiring low power usage and real-time performance, such as in gadgets. Their smaller size allows for faster training, easier deployment on embedded devices, and continuous updating over-the-air. Recent advances in small networks like SqueezeNet achieve similar accuracy as larger networks but with much smaller size and parameters.
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Harro Stokman, Chief Executive Officer and Founder of Kepler Vision Technologies, presents the “Improving Nursing Care with Privacy-Sensitive Edge Computer Vision” tutorial at the May 2021 Embedded Vision Summit.
Around the world, there is a serious and growing shortage of nurses. Nursing care at night is a particular challenge because night shifts are less attractive to nurses and since patients’ needed rest can be disturbed by in-person monitoring. Computer-vision-based activity detection provides the ability to reliably monitor patients and alert nurses when assistance is needed.
But creating and deploying a solution requires overcoming several significant obstacles. For example, a single overhead camera with a fisheye lens capable of viewing an entire room delivers very distorted images. In addition, privacy is a critical concern. And, care facilities often have minimal IT infrastructure and staff. In this talk, Dr. Harro Stokman explains how his company’s Kepler Night Nurse product has overcome these challenges and achieved registration as a medical device."Approaches for Energy Efficient Implementation of Deep Neural Networks," a P...
"Approaches for Energy Efficient Implementation of Deep Neural Networks," a P...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2018-embedded-vision-summit-sze
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Associate Professor at MIT, presents the "Approaches for Energy Efficient Implementation of Deep Neural Networks" tutorial at the May 2018 Embedded Vision Summit.
Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks. But these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them.
This talk explores how DNNs are being mapped onto today’s processor architectures, and how these algorithms are evolving to enable improved efficiency. Sze explores the energy consumption of commonly used CNNs versus their accuracy, and provides insights on "energy-aware" pruning of these networks.“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
“Introduction to DNN Model Compression Techniques,” a Presentation from XailientEdge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/introduction-to-dnn-model-compression-techniques-a-presentation-from-xailient/
Sabina Pokhrel, Customer Success AI Engineer at Xailient, presents the “Introduction to DNN Model Compression Techniques” tutorial at the May 2021 Embedded Vision Summit.
Embedding real-time large-scale deep learning vision applications at the edge is challenging due to their huge computational, memory, and bandwidth requirements. System architects can mitigate these demands by modifying deep-neural networks to make them more energy efficient and less demanding of processing resources by applying various model compression approaches.
In this talk, Pokhrel provides an introduction to four established techniques for model compression. She discusses network pruning, quantization, knowledge distillation and low-rank factorization compression approaches.“Secure Hardware Architecture for Embedded Vision,” a Presentation from Neuro...
“Secure Hardware Architecture for Embedded Vision,” a Presentation from Neuro...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/secure-hardware-architecture-for-embedded-vision-a-presentation-from-neurobinder/
Jonathan Cefalu, CEO and founder of NeuroBinder, presents the “Secure Hardware Architecture for Embedded Vision” tutorial at the May 2021 Embedded Vision Summit.
Security is a problem for every IoT system, but due to privacy concerns, security is particularly worrisome for embedded vision systems. In this talk, Cefalu covers how to design your embedded device so that the hardware architecture itself enforces strict guarantees about where visual data is able to flow.
In particular, Cefalu explores the idea of a “data diode” that uses hardware to enforce what parts of the system have access to video or images. This provides the highest level of protection against hacking or malware, as even if the device is completely hacked at the root level, the intruder will still be unable to access the visual data."Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/mathworks/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-hiremath-chou
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Sandeep Hiremath, Product Manager, and Bill Chou, Senior Computer Vision Scientist, both of MathWorks, present the "Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB" tutorial at the May 2019 Embedded Vision Summit.
In this presentation, Hiremath and Chou explain how to bring the power of deep neural networks to memory- and power-constrained devices like those used in robotics and automated driving. The workflow starts with an algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease of use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB.
Next, the networks are trained using MATLAB’s GPU and parallel computing support either on the desktop, a local compute cluster or in the cloud. In the deployment phase, code generation tools are employed to automatically generate optimized code that can target both embedded GPUs like Jetson, Jetson Drive AGX Xavier, Intel-based CPU platforms or ARM-based embedded platforms. The generated code leverages target-specific libraries that are highly optimized for the target architecture and memory model.“Introduction to Simultaneous Localization and Mapping (SLAM),” a Presentatio...
“Introduction to Simultaneous Localization and Mapping (SLAM),” a Presentatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/introduction-to-simultaneous-localization-and-mapping-slam-a-presentation-from-gareth-cross/
Independent game developer (and former technical lead of state estimation at Skydio) Gareth Cross presents the “Introduction to Simultaneous Localization and Mapping (SLAM)” tutorial at the May 2021 Embedded Vision Summit.
This talk provides an introduction to the fundamentals of simultaneous localization and mapping (SLAM). Cross aims to provide foundational knowledge, and viewers are not expected to have any prerequisite experience in the field.
The talk consists of an introduction to the concept of SLAM, as well as practical design considerations in formulating SLAM problems. Visual inertial odometry is introduced as a motivating example of SLAM, and Cross explains how this problem is structured and solved.Introducing google’s mobile nets
Google announces the open source of MobileNe : Primarily focus on optimizing for latency but also yield small networks. https://arxiv.org/abs/1704.04861
This material is to serve as guide reading of the paper.
Machine Learning for Weather Forecasts
In this deck from the Stanford HPC Conference, Peter Dueben from the European Centre for Medium-Range Weather Forecasts (ECMWF) presents: Machine Learning for Weather Forecasts.
"I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will than talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future."
Peter is contributing to the development and optimization of weather and climate models for modern supercomputers. He is focusing on a better understanding of model error and model uncertainty, on the use of reduced numerical precision that is optimised for a given level of model error, on global cloud- resolving simulations with ECMWF's forecast model, and the use of machine learning, and in particular deep learning, to improve the workflow and predictions. Peter has graduated in Physics and wrote his PhD thesis at the Max Planck Institute for Meteorology in Germany. He worked as Postdoc with Tim Palmer at the University of Oxford and has taken up a position as University Research Fellow of the Royal Society at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2017.
Watch the video: https://youtu.be/ks3fkRj8Iqc
Learn more: https://www.ecmwf.int/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
EfficientNet
The document proposes a compound scaling method to scale neural networks efficiently along their depth, width, and resolution dimensions simultaneously. It introduces EfficientNets, a new family of models created by applying this compound scaling technique to a baseline architecture found using neural architecture search. Evaluation shows that EfficientNets outperform previous state-of-the-art convolutional neural networks like ResNet and MobileNet in terms of accuracy and efficiency.
"Machine Learning- based Image Compression: Ready for Prime Time?," a Present...
"Machine Learning- based Image Compression: Ready for Prime Time?," a Present...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-gormish
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Michael Gormish, Research Manager at Clarifai, presents the "Machine Learning- based Image Compression: Ready for Prime Time?" tutorial at the May 2019 Embedded Vision Summit.
Computer vision is undergoing dramatic changes because deep learning techniques are now able to solve complex non-linear problems. Computer vision pipelines used to consist of hand engineered stages mathematically optimized for some carefully chosen objective function. These pipelines are being replaced with machine- learned stages or end-to-end learning techniques where enough ground truth data is available.
Similarly, for decades image compression has relied on hand crafted algorithm pipelines, but recent efforts using deep learning are reporting higher image quality than that provided by conventional techniques. Is it time to replaced discrete cosine transforms with machine-learned compression techniques?
This talk examines practical aspects of deep learned image compression systems as compared with traditional approaches. Gormish considers memory, computation and other aspects, in addition to rate-distortion, to see when ML-based compression should be considered or avoided. He also discusses approaches using a combination of machine learned and traditional techniques."Deep Learning for Manufacturing Inspection Applications," a Presentation fro...
"Deep Learning for Manufacturing Inspection Applications," a Presentation fro...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-se
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Stephen Se, Research Manager at FLIR Systems, presents the "Deep Learning for Manufacturing Inspection Applications" tutorial at the May 2019 Embedded Vision Summit.
Recently, deep learning has revolutionized artificial intelligence and has been shown to provide the best solutions to many problems in computer vision, image classification, speech recognition and natural language processing. Se presents his company's deep learning activities for machine vision applications such as manufacturing inspection, defect detection and classification.
Deep learning has gained significant attention in the machine vision industry because it does not require the complex algorithm development used by traditional rule-based image processing techniques. Se cover the deep learning workflow from data collection to training and deployment, as well as the process of transfer learning. He presents two case studies where his company applies transfer learning to manufacturing inspection applications.“An Introduction to Single-Photon Avalanche Diodes—A New Type of Imager for C...
“An Introduction to Single-Photon Avalanche Diodes—A New Type of Imager for C...Edge AI and Vision Alliance
Single-photon avalanche diodes (SPADs) are novel sensors that can detect individual photons with high time resolution. SPADs allow for imaging with extreme dynamic range from low to high light conditions without saturation. They also enable minimal motion blur imaging due to their ability to precisely timestamp single photons. Recent research has demonstrated burst photography using SPAD arrays that can reconstruct non-rigid scene motion and produce almost motion-blur free images in dark environments. However, challenges remain in increasing resolution, reducing data rates and power consumption before widespread commercial applications can be realized.“Case Study: Facial Detection and Recognition for Always-On Applications,” a ...
“Case Study: Facial Detection and Recognition for Always-On Applications,” a ...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/case-study-facial-detection-and-recognition-for-always-on-applications-a-presentation-from-synopsys/
Jamie Campbell, Product Marketing Manager for Embedded Vision IP at Synopsys, presents the “Case Study: Facial Detection and Recognition for Always-On Applications” tutorial at the May 2021 Embedded Vision Summit.
Although there are many applications for low-power facial recognition in edge devices, perhaps the most challenging to design are always-on, battery-powered systems that use facial recognition for access control. Laptop, tablet and cellphone users expect hands-free and instantaneous facial recognition. This means the electronics must be always on, constantly looking to detect a face, and then ready to pull from a data set to recognize the face.
This presentation describes the challenges of moving traditional facial detection neural networks to the edge. It explores a case study of a face recognition access control application requiring continuous operation and extreme energy efficiency. Finally, it describes how the combination of Synopsys DesignWare ARC EM and EV processors provides low-power, efficient DSP and CNN acceleration for this application."Quantizing Deep Networks for Efficient Inference at the Edge," a Presentatio...
"Quantizing Deep Networks for Efficient Inference at the Edge," a Presentatio...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.Enabling Power-Efficient AI Through Quantization
Artificial Intelligence (AI), specifically deep learning, is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, the deep neural networks of today use too much memory, compute, and energy. Plus, to make AI truly ubiquitous, networks need to run on the end device within a tight power and thermal budget. One approach to help address these issues is quantization, which attempts to reduce the number of bits used for weight parameters and activation calculations without sacrificing model accuracy. This presentation covers: why quantization is important, existing quantization challenges, Qualcomm AI Research's existing quantization research, and how developers and researchers can take advantage of quantization on Qualcomm Snapdragon.
“Efficient Deep Learning for 3D Point Cloud Understanding,” a Presentation fr...
“Efficient Deep Learning for 3D Point Cloud Understanding,” a Presentation fr...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/efficient-deep-learning-for-3d-point-cloud-understanding-a-presentation-from-facebook/
Bichen Wu, Research Scientist at Facebook Reality Labs, presents the “Efficient Deep Learning for 3D Point Cloud Understanding” tutorial at the May 2021 Embedded Vision Summit.
Understanding the 3D environment is a crucial computer vision capability required by a growing set of applications such as autonomous driving, AR/VR and AIoT. 3D visual information, captured by LiDAR and other sensors, is typically represented by a point cloud consisting of thousands of unstructured points.
Developing computer vision solutions to understand 3D point clouds requires addressing several challenges, including how to efficiently represent and process 3D point clouds, how to design efficient on-device neural networks to process 3D point clouds, and how to easily obtain data to train 3D models and improve data efficiency. In this talk, Wu shows how his company addresses these challenges as part of its “SqeezeSeg” research and presents a highly efficient, accurate, and data-efficient solution for on-device 3D point-cloud understanding.AI+ Remote Sensing: Applying Deep Learning to Image Enhancement, Analytics, a...
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A Keynote Speech in DigitalWorld Congress 2021.
Youtube Video https://www.youtube.com/watch?v=FdUyaUJTYLY
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group. 20211118 AI+ Remote Sensing
In this talk, we introduce our proposed AI+ Remote Sensing techniques from the Research Lab of Ping An Technology. One of the techniques is our deep learning haze removal model which can effectively remove the interference of haze in the satellite images and observe the true ground reflectance. Next, we introduce our super-resolution model which can enhance 4x image details. The SR model has been deployed to the Sentinel-2 satellite imagery and greatly improve its image quality. Last, we introduce our crop recognition system. The system includes a user interface for a user to label a few of training samples, and the proposed crop recognition model can be trained on the fly to be deployed on a broad geo-area immediately. In addition to the techniques, our AI+ Remote Sensing technologies have been supporting the carbon(CO2) emission analysis for Environment, Society, and Government(ESG) Department, flooding and disaster analysis for Smart City Department, and crop field forecast for Investment Department in Ping An Group.
Joint super resolution and denoising from a single depth image
IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar
MobileNet Review | Mobile Net Research Paper Review | MobileNet v1 Paper Expl...
Hi, In this lesson I will discuss how you can read a research paper and I will explain the MobileNet research paper published in 2017. I will first show you paper and the will present key findings through a .PPT presentation. I hope you would find it useful and like this video.
Learn Complete Data Science with these 5 video series.
1. Python for Beginners
https://www.youtube.com/watch?v=b42eTWkEIfA&list=PLc2rvfiptPSRmd4eWpRmzRIPebX3W9mju
2. Machine Learning for Beginners
https://www.youtube.com/watch?v=ZeM2tHtjGy4&list=PLc2rvfiptPSTvPFbNlT_TGRupzKKhJSIv
3. Feature Selection in Machine Learning
https://www.youtube.com/watch?v=kA4mD3y4aqA&list=PLc2rvfiptPSQYzmDIFuq2PqN2n28ZjxDH
4. Deep Learning with TensorFlow 2.0 and Keras
https://www.youtube.com/watch?v=nVvhkVLh60o&list=PLc2rvfiptPSR3iwFp1VHVJFK4yAMo0wuF
5. Natural Language Processing (NLP) Tutorials
https://www.youtube.com/watch?v=mrF9MD56-wk&list=PLc2rvfiptPSQgsORc7iuv7UxhbRJox-pW&index=1
The working code is given in the video description of each video. You can download the Jupyter notebook from GitHub.
Please Like and Subscribe to show your support.
Like Facebook Page:
https://www.facebook.com/kgptalkie/
Make Your Own Automated Email Marketing Software in Python
https://www.youtube.com/watch?v=gmYuom6kfoY&list=PLc2rvfiptPSQK9ErKaLqf40iu1A3le9Zr
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"Deploying Deep Learning Models on Embedded Processors for Autonomous Systems...
“Introduction to Simultaneous Localization and Mapping (SLAM),” a Presentatio...
“Introduction to Simultaneous Localization and Mapping (SLAM),” a Presentatio...
"Machine Learning- based Image Compression: Ready for Prime Time?," a Present...
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"Deep Learning for Manufacturing Inspection Applications," a Presentation fro...
"Deep Learning for Manufacturing Inspection Applications," a Presentation fro...
“An Introduction to Single-Photon Avalanche Diodes—A New Type of Imager for C...
“An Introduction to Single-Photon Avalanche Diodes—A New Type of Imager for C...
“Case Study: Facial Detection and Recognition for Always-On Applications,” a ...
“Case Study: Facial Detection and Recognition for Always-On Applications,” a ...
"Quantizing Deep Networks for Efficient Inference at the Edge," a Presentatio...
"Quantizing Deep Networks for Efficient Inference at the Edge," a Presentatio...
“Efficient Deep Learning for 3D Point Cloud Understanding,” a Presentation fr...
“Efficient Deep Learning for 3D Point Cloud Understanding,” a Presentation fr...
AI+ Remote Sensing: Applying Deep Learning to Image Enhancement, Analytics, a...
AI+ Remote Sensing: Applying Deep Learning to Image Enhancement, Analytics, a...
Joint super resolution and denoising from a single depth image
Joint super resolution and denoising from a single depth image
MobileNet Review | Mobile Net Research Paper Review | MobileNet v1 Paper Expl...
MobileNet Review | Mobile Net Research Paper Review | MobileNet v1 Paper Expl...
Similar to "Designing CNN Algorithms for Real-time Applications," a Presentation from Almond AI
“High-fidelity Conversion of Floating-point Networks for Low-precision Infere...
“High-fidelity Conversion of Floating-point Networks for Low-precision Infere...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/high-fidelity-conversion-of-floating-point-networks-for-low-precision-inference-using-distillation-with-limited-data-a-presentation-from-imagination-technologies/
James Imber, Senior Research Engineer at Imagination Technologies, presents the “High-fidelity Conversion of Floating-point Networks for Low-precision Inference using Distillation with Limited Data” tutorial at the May 2021 Embedded Vision Summit.
When converting floating-point networks to low-precision equivalents for high-performance inference, the primary objective is to maximally compress the network whilst maintaining fidelity to the original, floating-point network. This is made particularly challenging when only a reduced or unlabelled dataset is available. Data may be limited for reasons of a commercial or legal nature: for example, companies may be unwilling to share valuable data and labels that represent a substantial investment of resources; or the collector of the original dataset may not be permitted to share it for data privacy reasons.
Imber presents a method based on distillation that allows high-fidelity, low-precision networks to be produced for a wide range of different network types, using the original trained network in place of a labeled dataset. The proposed approach is directly applicable across multiple domains (e.g. classification, segmentation and style transfer) and can be adapted to numerous network compression techniques.Small Deep-Neural-Networks: Their Advantages and Their Design
This document discusses small deep neural networks, their advantages, and their design. It notes that computer vision tasks now work well due to advances in deep learning. Small neural networks have advantages for applications requiring low power usage and real-time performance, such as in gadgets. Their smaller size allows for faster training, easier deployment on embedded devices, and continuous updating over-the-air. Recent advances in small networks like SqueezeNet achieve similar accuracy as larger networks but with much smaller size and parameters.
PointNet
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Scalable Graph Convolutional Network Based Link Prediction on a Distributed G...
Graph Convolutional Networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices and edges with rich types of attributes is a significant issue to be addressed due to the storage and computation limitations of the machines. In this paper we present a scalable link prediction approach which conducts GCN training and link prediction on top of a distributed graph database server called JasmineGraph. We partition graph data and persist them in multiple workers. We implement parallel graph node embedding generation using GraphSAGE algorithm in multiple workers. Our approach avoids facing performance bottlenecks in GCN training using an intelligent scheduling algorithm. We show our approach scales well with an increasing number of partitions (2,4,8, and 16) using four real world data sets called Twitter, Amazon, Reddit, and DBLP-V11. JasmineGraph was able to train a GCN from the largest dataset DBLP-V11 ($>$ 9.3GB) in 11 hours and 40 minutes time using 16 workers on a single server while the original GraphSAGE implementation could not process it at all. The original GraphSAGE implementation processed the second largest dataset Reddit in 238 minutes while JasmineGraph took only 100 minutes on the same hardware with 16 workers leading to 2.4 times improved performance.
AI On the Edge: Model Compression
Inference on edge has an ever increasing performance for companies and thus it is crucial to be able to make models smaller. Compressing models can be loss-less or can result in loss of accuracy. This presentation provides a survey of compression techniques for deep learning models. It then describes different architectures of AWS IoT/Green Grass to combine on-device inference and GPU inference in a hub model. Additionally the presentation introduces MXNet, which has small footprint and efficient both for inference and training in distributed settings.
Scaling Deep Learning Algorithms on Extreme Scale Architectures
This document summarizes a presentation on scaling deep learning algorithms on extreme scale architectures. It discusses challenges in using deep learning, a vision for machine/deep learning R&D including novel algorithms, and the MaTEx toolkit which supports distributed deep learning on GPU and CPU clusters. Sample results show strong and weak scaling of asynchronous gradient descent on Summit. Fault tolerance needs and the impact of deep learning on other domains are also covered.
Efficient de cvpr_2020_paper
This document proposes EfficientDet, a new family of object detectors that achieve better accuracy and efficiency across a wide range of resource constraints. The key contributions are:
1. A weighted bi-directional feature pyramid network (BiFPN) that introduces learnable weights to efficiently fuse multi-scale features from different levels.
2. A compound scaling method that jointly scales the resolution, depth, and width of the backbone, feature network, and box/class prediction networks for higher accuracy.
3. Combining EfficientNet backbones with BiFPN and compound scaling, EfficientDet achieves state-of-the-art 52.2% AP on COCO while being 4x smaller and using 13x
PR-183: MixNet: Mixed Depthwise Convolutional Kernels
TensorFlow-KR 논문읽기모임 PR12(12PR) 183번째 논문 review입니다.
이번에 살펴볼 논문은 Google Brain에서 발표한 MixNet입니다. Efficiency를 추구하는 CNN에서 depthwise convolution이 많이 사용되는데, 이 때 depthwise convolution filter의 size를 다양하게 해서 성능도 높이고 efficiency도 높이는 방법을 제안한 논문입니다. 자세한 내용은 영상을 참고해주세요
논문링크 : https://arxiv.org/abs/1907.09595
발표영상 : https://youtu.be/252YxqpHzsg
ML Paper Tutorial - Video Face Manipulation Detection Through Ensemble of CNN...
This is a slide deck for paper review. This paper is about using a machine learning algorithm to detect deepfake images.
Development of 3D convolutional neural network to recognize human activities ...
This document describes the development of a 3D convolutional neural network (CNN) model to recognize human activities using moderate computation capabilities. The model is trained on the KTH dataset, which contains activities like walking, running, jogging, handwaving, handclapping, and boxing. The proposed model uses 3D CNN layers and max pooling layers to extract both spatial and temporal features from video frames. Testing achieved an accuracy of 93.33% for activity recognition. The number of model parameters and operations are also calculated to show the model can perform human activity recognition with reasonable computational requirements suitable for devices with moderate capabilities.
AI optimizing HPC simulations (presentation from 6th EULAG Workshop)
See our presentation from the 6th International EULAG Users Workshop. We talked about taking HPC to the "Industry 4.0" by implementing smart techniques to optimize the codes in terms of performance and energy consumption. It explains how Machine Learning can dynamically optimize HPC simulations and byteLAKE's software autotuning solution.
Find out more about byteLAKE at: www.byteLAKE.com
ResNeSt: Split-Attention Networks
This document proposes ResNeSt, a split-attention network that divides feature maps into groups and applies attention mechanisms across groups. It outperforms ResNet variants on image classification, object detection, semantic segmentation, and instance segmentation while maintaining the same computational efficiency. The paper introduces ResNeSt's split attention block, training strategies including large batches, data augmentation, and regularization methods. Evaluation shows ResNeSt achieves state-of-the-art accuracy on ImageNet and downstream tasks using less computation than NAS models.
RunPool: A Dynamic Pooling Layer for Convolution Neural Network
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
Accelerating stochastic gradient descent using adaptive mini batch size3
This document proposes a method called Train-Measure-Adapt-Repeat for accelerating stochastic gradient descent training of deep neural networks using adaptive mini-batch sizes. The method starts with an extremely small mini-batch size, such as 4-8 samples, to allow for faster training initially through more frequent weight updates. Accuracy is evaluated over time rather than by the number of steps, and the mini-batch size is increased adaptively when accuracy improvements stall. Experiments on image classification datasets demonstrate the method reaching higher accuracy levels faster than using fixed large mini-batch sizes.
lec6a.ppt
Convolutional neural networks can be used for handwritten digit recognition. They employ replicated feature detectors with shared weights to achieve translation equivariance. Pooling layers provide some translation invariance while reducing the number of inputs to subsequent layers. The LeNet architecture developed by Yann LeCun used these techniques along with multiple hidden layers and achieved an error rate of around 1% on handwritten digit recognition. Dropout regularization helps convolutional neural networks generalize well when applied to large scale tasks like ImageNet classification by preventing complex co-adaptations between hidden units.
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...
"Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedde...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/luxoft/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Alexey Rybakov, Senior Director for Embedded Systems at Luxoft, presents the "Deep Learning Beyond Cats and Cars: Developing a Real-life DNN-based Embedded Vision Product for Agriculture, Construction, Medical, or Retail" tutorial at the May 2017 Embedded Vision Summit.
By now we know very well how to design and train a neural network to recognize cats, dogs and cars. But what about real projects — for example, in agriculture, construction, medical, and retail? This how-to talk provides an overview of what it takes to design, train, and fine-tune a real-life DNN-based embedded vision solution. Rybakov explores algorithmic, data set, training, and optimization decisions that take you from proofs-of-concepts to solid, reliable, and highly optimized systems. This material is based on Luxoft's own successes, failures, and lessons learned while implementing embedded vision solutions.Improving Hardware Efficiency for DNN Applications
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Computer Vision for Beginners
This document provides an overview of computer vision techniques including classification and object detection. It discusses popular deep learning models such as AlexNet, VGGNet, and ResNet that advanced the state-of-the-art in image classification. It also covers applications of computer vision in areas like healthcare, self-driving cars, and education. Additionally, the document reviews concepts like the classification pipeline in PyTorch, data augmentation, and performance metrics for classification and object detection like precision, recall, and mAP.
IRJET - Factors Affecting Deployment of Deep Learning based Face Recognition ...
This document discusses factors affecting the deployment of deep learning models for face recognition on smartphones. It examines training data requirements, suitable neural network architectures, and effective loss functions. Larger datasets with more subjects and images are preferred for training models that generalize well. Residual networks like ResNet have achieved good accuracy while being efficient for face recognition. Loss functions like center loss and triplet loss help learn discriminative features by reducing intra-class and increasing inter-class variations.
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETS
The ability to mine and extract useful information automatically, from large datasets, is a
common concern for organizations (having large datasets), over the last few decades. Over the
internet, data is vastly increasing gradually and consequently the capacity to collect and store
very large data is significantly increasing.
Existing clustering algorithms are not always efficient and accurate in solving clustering
problems for large datasets.
However, the development of accurate and fast data classification algorithms for very large
scale datasets is still a challenge. In this paper, various algorithms and techniques especially,
approach using non-smooth optimization formulation of the clustering problem, are proposed
for solving the minimum sum-of-squares clustering problems in very large datasets. This
research also develops accurate and real time L2-DC algorithm based with the incremental
approach to solve the minimum
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Small Deep-Neural-Networks: Their Advantages and Their Design
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FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETS
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.“Silicon Slip-Ups: The Ten Most Common Errors Processor Suppliers Make (Numbe...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/silicon-slip-ups-the-ten-most-common-errors-processor-suppliers-make-number-four-will-amaze-you-a-presentation-from-bdti/
Phil Lapsley, Co-founder and Vice President of BDTI, presents the “Silicon Slip-Ups: The Ten Most Common Errors Processor Suppliers Make (Number Four Will Amaze You!)” tutorial at the May 2024 Embedded Vision Summit.
For over 30 years, BDTI has provided engineering, evaluation and advisory services to processor suppliers and companies that use processors in products. The company has seen a lot, including some classic mistakes. (You know, things like: the chip has an accelerator, but no easy way to program it… or you can only program it using an obscure proprietary framework. Or it has an ISP that only works with one image sensor. Or the development tools promise a lot but fall far short. Or the device drivers don’t work. Or the documentation is deficient.)
Phil Lapsley, BDTI co-founder, presents a fun and fast-paced review of some of the most common processor provider errors, ones seen repeatedly at BDTI. If you’re a processor provider, you’ll learn things you can do to avoid these goofs—and if you’re a processor user, you’ll learn about things to watch for when selecting your next processor!“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
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Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.“How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge,...
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Stephen Su, Senior Segment Marketing Manager at Arm, presents the “How Arm’s Machine Learning Solution Enables Vision Transformers at the Edge” tutorial at the May 2024 Embedded Vision Summit.
AI at the edge has been transforming over the last few years, with newer use cases running more efficiently and securely. Most edge AI workloads were initially run on CPUs, but machine learning accelerators have gradually been integrated into SoCs, providing more efficient solutions. At the same time, ChatGPT has driven a sudden surge in interest in transformer-based models, which are primarily deployed using cloud resources. Soon, many transformer-based models will be modified to run effectively on edge devices.
In this presentation, Su explains the role of transformer-based models in vision applications and the challenges of implementing transformer models at the edge. Next, he introduces the latest Arm machine learning solution and how it enables the deployment of transformer-based vision networks at the edge. Finally, he shares an example implementation of a transformer-based embedded vision use case and uses this to contrast such solutions with those based on traditional CNN networks.“Nx EVOS: A New Enterprise Operating System for Video and Visual AI,” a Prese...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/nx-evos-a-new-enterprise-operating-system-for-video-and-visual-ai-a-presentation-from-network-optix/
Nathan Wheeler, Co-founder and CEO of Network Optix, presents the “Nx EVOS: A New Enterprise Operating System for Video and Visual AI” tutorial at the May 2024 Embedded Vision Summit.
In most software domains, developers don’t write code at the bare-metal level; they build applications on top of operating systems, which provide commonly needed functionality. Yet, today, developers of video and AI applications are effectively writing their applications at the bare-metal level, building the “plumbing” themselves to handle basics like device discovery, storage management, security and model deployment. These developers need an operating system that supports their applications so they can focus on what really matters: the core functionality of their product.
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Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
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Xin Zhong, Computer Vision Product Manager at Qualcomm Technologies, presents the “OpenCV for High-performance, Low-power Vision Applications on Snapdragon” tutorial at the May 2024 Embedded Vision Summit.
For decades, the OpenCV software library has been popular for developing computer vision applications. However, developers have found it challenging to create efficient implementations of their OpenCV applications on processors optimized for edge applications, like the Qualcomm Snapdragon family. As part of its comprehensive support for computer vision application developers, Qualcomm provides a variety of tools to enable developers to take full advantage of the heterogeneous computing resources in the Snapdragon processors.
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Vinesh Sukumar, Senior Director of Product Management at Qualcomm Technologies, presents the “Deploying Large Models on the Edge: Success Stories and Challenges” tutorial at the May 2024 Embedded Vision Summit.
In this talk, Dr. Sukumar explains and demonstrates how Qualcomm has been successful in deploying large generative AI and multimodal models on the edge for a variety of use cases in consumer and enterprise markets. He examines key challenges that must be overcome before large models at the edge can reach their full commercial potential. He also highlights how Qualcomm is addressing these challenges through upgraded processor hardware, improved developer tools and a comprehensive library of fully optimized AI models in the Qualcomm AI Hub.“Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment,...
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Maurits Kaptein, Chief Data Scientist at Network Optix and Professor at the University of Eindhoven, presents the “Scaling Vision-based Edge AI Solutions: From Prototype to Global Deployment” tutorial at the May 2024 Embedded Vision Summit.
The Embedded Vision Summit brings together innovators in silicon, devices, software and applications and empowers them to bring computer vision and perceptual AI into reliable and scalable products. However, integrating recent hardware, software and algorithm innovations into prime-time-ready products is quite challenging. Scaling from a proof of concept—for example, a novel neural network architecture performing a valuable task efficiently on a new piece of silicon—to an AI vision system installed in hundreds of sites requires surmounting myriad hurdles.
First, building on Network Optix’s 14 years of experience, Professor Kaptein details how to overcome the networking, fleet management, visualization and monetization challenges that come with scaling a global vision solution. Second, Kaptein discusses the complexities of making vision AI solutions device-agnostic and remotely manageable, proposing an open standard for AI model deployment to edge devices. The proposed standard aims to simplify market entry for silicon manufacturers and enhance scalability for solution developers. Kaptein outlines the standard’s core components and invites collaborative contributions to drive market expansion.“What’s Next in On-device Generative AI,” a Presentation from Qualcomm
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Jilei Hou, Vice President of Engineering and Head of AI Research at Qualcomm Technologies, presents the “What’s Next in On-device Generative AI” tutorial at the May 2024 Embedded Vision Summit.
The generative AI era has begun! Large multimodal models are bringing the power of language understanding to machine perception, and transformer models are expanding to allow machines to understand using multiple types of sensors. This new wave of approaches is poised to revolutionize user experiences, disrupt industries and enable powerful new capabilities. For generative AI to reach its full potential, however, we must deploy it on edge devices, providing improved latency, pervasive interaction and enhanced privacy.
In this talk, Hou shares Qualcomm’s vision of the compelling opportunities enabled by efficient generative AI at the edge. He also identifies the key challenges that the industry must overcome to realize the massive potential of these technologies. And he highlights research and product development work that Qualcomm is doing to lead the way via an end-to-end system approach—including techniques for efficient on-device execution of LLMs, LVMs and LMMs, methods for orchestration of large models at the edge and approaches for adaptation and personalization.
“Learning Compact DNN Models for Embedded Vision,” a Presentation from the Un...
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Shuvra Bhattacharyya, Professor at the University of Maryland at College Park, presents the “Learning Compact DNN Models for Embedded Vision” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Bhattacharyya explores methods to transform large deep neural network (DNN) models into effective compact models. The transformation process that he focuses on—from large to compact DNN form—is referred to as pruning. Pruning involves the removal of neurons or parameters from a neural network. When performed strategically, pruning can lead to significant reductions in computational complexity without significant degradation in accuracy. It is sometimes even possible to increase accuracy through pruning.
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“Introduction to Computer Vision with CNNs,” a Presentation from Mohammad Hag...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/introduction-to-computer-vision-with-cnns-a-presentation-from-mohammad-haghighat/
Independent consultant Mohammad Haghighat presents the “Introduction to Computer Vision with Convolutional Neural Networks” tutorial at the May 2023 Embedded Vision Summit.
This presentation covers the basics of computer vision using convolutional neural networks. Haghighat begins by introducing some important conventional computer vision techniques and then transition to explaining the basics of machine learning and convolutional neural networks (CNNs) and showing how CNNs are used in visual perception.
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Parshad Patel, Data Scientist at Yummly, presents the “Selecting Tools for Developing, Monitoring and Maintaining ML Models” tutorial at the May 2023 Embedded Vision Summit.
With the boom in tools for developing, monitoring and maintaining ML models, data science teams have many options to choose from. Proprietary tools provided by cloud service providers are enticing, but teams may fear being locked in—and may worry that these tools are too costly or missing important features when compared with alternatives from specialized providers.
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Abdo Babukr, Accelerated Computing Consultant at Wave Spectrum, presents the “Building Accelerated GStreamer Applications for Video and Audio AI,” tutorial at the May 2023 Embedded Vision Summit.
GStreamer is a popular open-source framework for creating streaming media applications. Developers often use GStreamer to streamline the development of computer vision and audio perception applications. Since perceptual algorithms are often quite demanding in terms of processing performance, in many cases developers need to find ways to accelerate key GStreamer building blocks, taking advantage of specialized features of their target processor or co-processor.
In this talk, Babukr introduces GStreamer and shows how to use it to build computer vision and audio perception applications. He also shows how to create efficient, high-performance GStreamer applications that utilize specialized hardware features.“Understanding, Selecting and Optimizing Object Detectors for Edge Applicatio...
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/understanding-selecting-and-optimizing-object-detectors-for-edge-applications-a-presentation-from-walmart-global-tech/
Md Nasir Uddin Laskar, Staff Machine Learning Engineer at Walmart Global Tech, presents the “Understanding, Selecting and Optimizing Object Detectors for Edge Applications” tutorial at the May 2023 Embedded Vision Summit.
Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge.
In this presentation, Laskar explores the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. He delves into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examines their strengths and weaknesses. And he provides guidance on how to evaluate and select an object detector for an edge application.“Introduction to Modern LiDAR for Machine Perception,” a Presentation from th...
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Dinesh Jayaraman, Assistant Professor at the University of Pennsylvania, presents the “Vision-language Representations for Robotics” tutorial at the May 2023 Embedded Vision Summit.
In what format can an AI system best present what it “sees” in a visual scene to help robots accomplish tasks? This question has been a long-standing challenge for computer scientists and robotics engineers. In this presentation, Jayaraman provides insights into cutting-edge techniques being used to help robots better understand their surroundings, learn new skills with minimal guidance and become more capable of performing complex tasks.
Jayaraman discusses recent advances in unsupervised representation learning and explains how these approaches can be used to build visual representations that are appropriate for a controller that decides how the robot should act. In particular, he presents insights from his research group’s recent work on how to represent the constituent objects and entities in a visual scene, and how to combine vision and language in a way that permits effectively translating language-based task descriptions into images depicting the robot’s goals.“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsights
“ADAS and AV Sensors: What’s Winning and Why?,” a Presentation from TechInsightsEdge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/adas-and-av-sensors-whats-winning-and-why-a-presentation-from-techinsights/
Ian Riches, Vice President of the Global Automotive Practice at TechInsights, presents the “ADAS and AV Sensors: What’s Winning and Why?” tutorial at the May 2023 Embedded Vision Summit.
It’s clear that the number of sensors per vehicle—and the sophistication of these sensors—is growing rapidly, largely thanks to increased adoption of advanced safety and driver assistance features. In this presentation, Riches explores likely future demand for automotive radars, cameras and LiDARs.
Riches examines which vehicle features will drive demand out to 2030, how vehicle architecture change is impacting the market and what sorts of compute platforms these sensors will be connected to. Finally, he shares his firm’s vision of what the landscape could look like far beyond 2030, considering scenarios out to 2050 for automated driving and the resulting sensor demand.“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...
“Computer Vision in Sports: Scalable Solutions for Downmarkets,” a Presentati...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/10/computer-vision-in-sports-scalable-solutions-for-downmarkets-a-presentation-from-sportlogiq/
Mehrsan Javan, Co-founder and CTO of Sportlogiq, presents the “Computer Vision in Sports: Scalable Solutions for Downmarket Leagues” tutorial at the May 2023 Embedded Vision Summit.
Sports analytics is about observing, understanding and describing the game in an intelligent manner. In practice, this requires a fully automated, robust end-to-end pipeline, spanning from visual input, to player and group activities, to player and team evaluation to planning. Despite major advancements in computer vision and machine learning, today sports analytics solutions are limited to top leagues and are not widely available for downmarket leagues and youth sports.
In this talk, Javan explains how his company has developed scalable and robust computer vision solutions to democratize sport analytics and offer pro-league-level insights to leagues with modest resources, including youth leagues. He highlights key challenges—such as the requirement for low-cost, low-latency processing and the need for robustness despite variations in venues. He discusses the approaches Sportlogiq tried and how it ultimately overcame these challenges, including the use of transformers and fusion of multiple type of data streams to maximize accuracy.“Detecting Data Drift in Image Classification Neural Networks,” a Presentatio...
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1) The document proposes a novel statistical method to detect and estimate the magnitude of data drift in image classification neural networks. It considers various types of data drift including noise effects, weather effects, and camera degradation.
2) The method introduces a "threshold probability" for each class to determine the percentage of predictions above the threshold at different drift magnitudes. This allows estimating the drift magnitude based on the difference between expected and observed percentages.
3) Experimental results on MNIST digit classification show the method can accurately detect and estimate the magnitude of different types of data drift like Gaussian noise, Poisson noise, and Salt & Pepper noise. The proposed approach helps neural networks cope with data drifts during operation.More from Edge AI and Vision Alliance (20)
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5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Webinar: Designing a schema for a Data Warehouse
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
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Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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"Designing CNN Algorithms for Real-time Applications," a Presentation from Almond AI
- 1. Copyright © 2017 Almond Research Pte Ltd. 1 Matthew Chiu May 2nd 2017 Designing CNN Algorithms For Real- time Applications
- 2. Copyright © 2017 Almond Research Pte Ltd. 2 • Consider 3 stages: Dataset – Model – Deployment • Understand relationship between size (depth) and performance of Deep Neural Nets. • Share weights to reduce number of parameters and operations. • Reduce total number of computations during model inference by factoring the convolution layer Optimization Pipeline
- 3. Copyright © 2017 Almond Research Pte Ltd. 3 • Feature Sharing • “Distilling the Knowledge in a Neural Network” • Very deep networks: Inception, Resnet, Highway networks, etc. • Densely Connected Networks (2016) • Resnet, Spatially Adaptive Computation Time (2017) • Pruning, Weight Sharing, Compression • “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization, And Huffman Coding Relevant Research
- 4. Copyright © 2017 Almond Research Pte Ltd. 4 • 2D Convolutional Layer (multiply-add) • Not counting activation, biases, batch norm • Cost of Fully Convolutional Layers is a matrix multiply + bias • Well studied: Pooling, Factorization Review Computational Cost [height*width]*(k/ stride)2 *(#inmaps*#outmaps)
- 5. Copyright © 2017 Almond Research Pte Ltd. 5 Model Error Rate (Top 5) Depth Parameters Runtime (ms) AlexNet1 19.9 8 62M 5.32 VGG2 9.33 16 144M 46.16 Inception2 9.15 22 6.8M 11.94 Resnet - 1012 6.05 110 1.7M 53.28 Size vs. Performance ImageNet 1 ImageNet 2012 2 Imagenet 2014 3 Pascal Titan X, Torch, CUDNN 5, Forward Pass Only https://github.com/jcjohnson/cnn-benchmarks
- 6. Copyright © 2017 Almond Research Pte Ltd. 6 GoogleNet Inception Reduce number of filters, before applying 3x3 and 5x5 convolutions
- 7. Copyright © 2017 Almond Research Pte Ltd. 7 Densely Connected Networks (Huang 2016) • Factorize a convolutional layer into multiple (12) small blocks of filters. • The input for each block is a concatenation of all previous convolutional filters. • The output of each block is concatenated with the input. • Recombine filters maps using a 1x1 convolution at the end of each stage.
- 8. Copyright © 2017 Almond Research Pte Ltd. 8 Densely Connected: Lowest Error On CIFAR 10/100 (millions)
- 9. Copyright © 2017 Almond Research Pte Ltd. 9 Example of a Real-time CNN
- 10. Copyright © 2017 Almond Research Pte Ltd. 10 • Kaggle Competition 2016 : Estimating Facial Keypoints Real-time CNN : Finding Facial Keypoints Training set of 7049 faces, 96x96 grayscale Problem is to learn the (x,y) position of 15 facial keypoints: Eyes (6), Eyebrows (4), Nose, Lips (4)
- 11. Copyright © 2017 Almond Research Pte Ltd. 11 • Goal is to use all of the data, some incomplete, to train a bigger network • Have to train a separate network for each subset of facial keypoints. Use data augmentation (horizontal and vertical shifting). • Solution is to share weights between the networks. Dataset : Incomplete Data Facial Keypoints Train (Shifting) Validation Eyes (Corners) 1795 (6.6%) 449 Eyebrows 1753 (10%) 437 Mouth (Corners) 1801 (6.6%) 445 Smiley ☺ 5605 (10%) 1395 Complete 1717 (15%) 423
- 12. Copyright © 2017 Almond Research Pte Ltd. 12 • Train on 2100 faces, only the samples with all 15 features. • Baseline: 6 layers (3 convolutional, 3 Fully Connected) and train on 1700 complete cases only. • Network size is 32 – 64 – 128 – 500 Model I: Baseline Network
- 13. Copyright © 2017 Almond Research Pte Ltd. 13 Model II : Siamese Network 32 64 128 192 Concatenate output of Last FC Layers of Each Network *Network is trained sequentially on each task
- 14. Copyright © 2017 Almond Research Pte Ltd. 14 Model III: Shared Densely Connected Network Parameters: N=12, K=3 Each block learns 12 filters at a time. The output is concatenated to the input to increase the depth of the network. N = 24 in the final stage of shared network.
- 15. Copyright © 2017 Almond Research Pte Ltd. 15 Convolution Input Output N K Image Size Ops Params 2x2 32 56 8 x3 94x94 92M 10,476 1x1 56 48 1/2 (L2 Weight Regularization) 2x2 48 96 12 x4 47x47 82M 37,152 1x1 96 96 1/2 2x2 96 132 12 x3 23x23 28M 53,232 1x1 132 128 2x2 128 200 24 x3 23x23 70M 131,520 1x1 200 192 1/2 192 11x11 Densely Connected Model Parameters
- 16. Copyright © 2017 Almond Research Pte Ltd. 16 Model RMS Error (Training) RMS Error (Testing) Depth Operations Conv. Params Baseline 1.68 2.5 6 75M 61,760 Siamese 32- 64-128-192 1.80 2.07 9 192M 160,064 Siamese 48- 96-192-192 1.70 2.18 9 297M 286,176 Densely Connected 2.13 2.29 23 366M 232,380 Results on Facial Keypoint Task RMS = Root Mean Square
- 17. Copyright © 2017 Almond Research Pte Ltd. 17 • When a training set is small (1717 photos), must test whether the network can actually perform the task in general. For feature tracking, measure Translational Invariance: use a validation set with randomized horizontal and vertical shifting. • The Siamese model does the best with a Test RMS Error of 2.07. In comparison, the baseline was the worst at generalization. Increasing the amount of filters or adding more depth (Densely Connected) only increased overfitting because there is not enough training data. • The mean average error for Siamese model was already 0.75 pixels. Targeting sub-pixel accuracy can also increase risk of overfitting or “memorization”. Improving generalization is a more suitable goal. Results Analysis
- 18. Copyright © 2017 Almond Research Pte Ltd. 18 Results (Siamese 32-64-128-192) Predicted Ground Truth Predicted Ground Truth
- 19. Copyright © 2017 Almond Research Pte Ltd. 19 Frame Rates Deployment Benchmarks 0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00 450.00 500.00 Baseline Siamese Dense 476.02 105.10 36.18 TX1 (Caffe) TX1 (Caffe) 0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00 450.00 500.00 Baseline Siamese Dense 470.28 287.33 71.03 "Titan X (Theano)" "Titan X (Theano)" Batch size 32 Batch size 64
- 20. Copyright © 2017 Almond Research Pte Ltd. 20 • Ideally, we can train a very deep network with lots of training data. In reality, we must be careful about overfitting. • Siamese model is a way to increase the training set size while sharing weights (convolutional filters) to reduce the convolutional operations. • For a Densely Connected model, depth can be increased with a small increase in the total number of operations. • Actual performance on the runtime platform may vary depending on your choice of DNN library. Conclusions
- 21. Copyright © 2017 Almond Research Pte Ltd. 21 • https://www.kaggle.com/c/facial-keypoints-detection • http://danielnouri.org/notes/2014/12/17/using-convolutional-neural-nets-to-detect- facial-keypoints-tutorial/ • Spatially Adaptive Computation Time for Residual Networks. Figurnov, Collins, Zhu, Zhang, Huang, Vetrox, Salakhutdinov • Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization, And Huffman Coding • Distilling the Knowledge in a Neural Network. Geoffrey Hinton, Oriol Vinyals, Jeff Dean • Going Deeper With Convolutions. Szegedy et Al. • Densely Connected Convoutional Networks. Gao Huang, Zhuang Liu, Killian Q. Weinberger Resources
- 22. Copyright © 2017 Almond Research Pte Ltd. 22 • Conceptually complicated, but made of repeating blocks. At each convolutional layer, only learn n * constant number of filters k. [ (Input * k) + ((Input + k) * k) + … ((Input + (n-1)*k) * k) ] • O(runtime) = (Input*(n*k) + (n2*k2) / 2) * (image size * kernel size) • For fixed n*k < (input / 2), • Convolutional Layer with output = input layers • O(Input2 * (image size * kernel size)) Appendix : Analysis of Densely Connected Networks
- 23. Copyright © 2017 Almond Research Pte Ltd. 23 • Reduce amount of memory required to store weights (Pruning 10x, Weight Sharing 30x, Compression 50x) • Also reduces Energy Consumption • Speedup on Fully Connected Layers by Sparse Matrix Multiply Appendix: Weight Storage Compression