“Tools and Strategies for Quickly Building Effective Image Datasets,” a Presentation from BDTI
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/08/tools-and-strategies-for-quickly-building-effective-image-datasets-a-presentation-from-bdti/ Evan Juras, Computer Vision Engineer at BDTI, presents the “Tools and Strategies for Quickly Building Effective Image Datasets,” tutorial at the May 2021 Embedded Vision Summit. A common pain point when using machine learning for computer vision is the need to manually curate and label large quantities of training images. Depending on the application, thousands to millions of images are needed in order to capture the range of visual environments, angles, lighting conditions, and other variations that may be encountered in the field. To make a model accurate enough for real-world applications, significant effort must be invested in creating a training dataset. Fortunately, there are tools and strategies to help accelerate the task of creating an image dataset. In this presentation, Juras discusses strategies for quickly building a new dataset for training an object detection model and will review tools and methods for speeding up the process of curating and labeling images.
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Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”A Ensemble Learning-based No Reference QoE Model for User Generated Contents
The document proposes an ensemble learning-based no-reference quality of experience (QoE) model for user-generated video content. It extracts encoding and content features from videos and pools the features to train an ensemble of decision trees to predict mean opinion scores without access to the original videos. The model achieves high prediction performance with Pearson linear correlation coefficients over 0.9 on a test dataset compared to baseline methods. Key encoding features like bitrate and quantization parameters are most important inputs. The ensemble learning approach outperforms non-ensemble methods, providing a low-complexity solution to assess user-generated video quality.
BTP_ppt
The document proposes an image enhancement technique to aid the visually impaired. It aims to first apply various algorithms like zooming and contrast adjustment to individual images using OpenCV, then extend this approach to videos by generating images from video frames and applying the image processing technique. While initial results on images showed improvement, applying it to videos in real-time proved challenging as video clarity was not achieved and the process time could not be reduced sufficiently. Future work focuses on developing a low-cost Android application that can interface with a camera and perform real-time video enhancement.
Building a Custom Vision Model
This document outlines a 5-step process for building and deploying a custom computer vision model using Oracle Cloud Infrastructure Vision Service to identify planes in satellite images. The steps include: 1) storing training and testing image data in object storage, 2) creating a labeling dataset using an automated labeling tool, 3) creating a vision project to manage the model, 4) training an object detection model using the labeled data, and 5) testing the trained model on unlabeled images to evaluate accuracy. The example trains a model to identify planes in 735 satellite photos within 1 hour using this process.
Traffic Violation Detector using Object Detection
Traffic Violation Detector using Object Detectionshri ram murti smarak college of engineering,technology & research
This document outlines an object detection system for detecting traffic violations using vehicle number plate identification. It discusses introducing object detection and its applications in traffic management. It describes the problem statement of relying on manual labor. It then explains the modules used in the system like authentication, webcam operation, and AI training. It discusses using TensorFlow and GAN for the object detection model and COCO dataset for training. Finally, it provides examples of applications like OCR, self-driving cars, and tracking objects and concludes with improving number plate extraction for low light images.A survey on Measurement of Objective Video Quality in Social Cloud using Mach...
This document discusses using machine learning techniques for objective measurement of video quality in social cloud applications. It proposes a method using convolutional neural networks (CNNs) trained on a large dataset of videos evaluated by human observers to predict video quality. The CNN model extracts quality-related features from videos which are used to train a machine learning model. The model achieved high accuracy in predicting subjective quality outcomes for new videos. Machine learning provides an objective way to assess video quality that can benefit users and service providers by optimizing video streaming quality. The document reviews key concepts like video quality assessment, deep learning and CNNs, and the typical methodology used in machine learning-based video quality measurement.
Computer Vision with Deep Learning
The document discusses computer vision with deep learning. It provides an overview of convolutional neural networks and their use in computer vision applications like image classification and object detection. Specifically, it discusses how CNNs use convolutional layers to learn visual features from images and provide examples of CNNs being used for pipeline defect classification and filler cap quality control.
Object detection presentation
The document describes a project that aims to develop a mobile application for real-time object and pose detection. The application will take in a real-time image as input and output bounding boxes identifying the objects in the image along with their class. The methodology involves preprocessing the image, then using the YOLO framework for object classification and localization. The goals are to achieve high accuracy detection that can be used for applications like vehicle counting and human activity recognition.
Deep Learning Projects - Anomaly Detection Using Deep Learning
Deep Learning Projects- Learn how to use state-of-the-art deep learning methods and autoencoders for anomaly detection.
Check out other interesting deep learning project ideas here - https://www.dezyre.com/projects/data-science-projects/deep-learning-projects
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Hila Blecher-Segev, Computer Vision and AI Research Associate at Vision Elements, presents the “Applying the Right Deep Learning Model with the Right Data for Your Application” tutorial at the May 2021 Embedded Vision Summit.
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What type of data will be most beneficial for the task? Should the DNN use other types of data in addition to images? How should the data be annotated? What classes should be defined? What is the minimum amount of data needed for the network to be generalized and robust? What algorithmic approach should we use for our task (classification, regression or segmentation)? What type of network should we choose (FCN, DCNN, RNN, GAN)? Blecher-Segev explains the options and trade-offs, and maps out a process for making good choices for a specific application.“Practical Image Data Augmentation Methods for Training Deep Learning Object ...
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Evan Juras, Computer Vision Engineer at EJ Technology Consultants, presents the “Practical Image Data Augmentation Methods for Training Deep Learning Object Detection Models” tutorial at the September 2020 Embedded Vision Summit.
Data augmentation is a method of expanding deep learning training datasets by making various automated modifications to existing images in the dataset. The resulting increased data diversity can enable a more accurate and robust model without the need to manually obtain more images.
In this presentation, Juras explores practical methods of image data augmentation for training object detection models. He also shows how to create an augmented dataset of 50,000 unique images with labeled bounding boxes in a few hours using a short Python script.
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.comUSING IMAGE CLASSIFICATION TO INCENTIVIZE RECYCLING
This document discusses using image classification to incentivize recycling. It proposes a web application where users can upload images of recyclable materials. Using image processing and classification algorithms, the material is identified and points are awarded. When enough points are accumulated, users can exchange them for rewards. The system architecture includes image upload and classification, data storage, and transaction processing. Popular classification models like ResNet and FastAI are evaluated. Analysis shows some materials like plastic and metal are confused, indicating room for improvement. The goal is to promote recycling through gamification and make recycling more accessible.
lokesh_UX_Designer_v5
This document contains the resume of Lokesh S, including his contact information, objective, skills, qualifications, and work experience. It summarizes several projects he worked on at previous companies, including smart parking, home automation, and fleet management mobile applications. For each project, it lists his role and responsibilities such as requirements analysis, user research, information architecture, and prototype design. It also includes references from previous managers and descriptions of additional projects at Honeywell involving tools for areas like lighting systems, security, and facilities management.
Computer architecture for vision system
Computer vision involves acquiring, processing, analyzing and understanding digital images to extract information from the real world. It has evolved from early research to now being used in thousands of applications across many industries. The architecture for computer vision systems requires different computational resources to handle different tasks efficiently, from low-level operations on images to high-level decision making. Key components include cameras, processors, and software like OpenCV and CUDA. Common applications include automotive safety systems, surveillance, medical imaging, and more.
FYP-Final-External
This document discusses using region of interest (ROI) with High Efficiency Video Coding (HEVC) to compress aerial videos. It proposes identifying ROIs in aerial images, applying HEVC encoding tailored to each ROI for better resolution and lower bandwidth usage, and transmitting the encoded video. The system would segment images, encode the tailored images using HEVC, and transmit them via FTP for viewing. It presents the scope, limitations, problem statement, tools and technologies used, system modules, use cases, data flow diagrams, algorithms, and conclusions of the proposed work. Future work could expand it to video conferencing by making faces the ROI.
Kyligence Cloud 4 - Feature Focus: AI-Augmented Engine
If you have big data, more and more of your analytics stack needs to be intelligent. Your tools need to be able to anticipate the needs of your analysts, customers, and your business. With the AI-Augmented Engine, this learning process is automated and predictive. It intelligently adapts to user behavior and query patterns and learns to anticipate each users’ needs. Join us for the third installment of this series diving into the core features of Kyligence Cloud 4.
In this presentation you will learn:
-How the Kyligence Cloud 4 AI-Augmented Engine works
-How the AI-Augmented Engine gives optimal efficiency for cube building
-How the AI-Augmented Engine greatly simplifies data modeling
Watch the webinar here: https://www.brighttalk.com/webcast/18317/480320
Deep learning with_computer_vision
This document outlines the course contents for a seminar on deep learning with computer vision. The 3-day course will cover topics such as convolutional neural networks, generative models, object detection, semantic segmentation, and applications of computer vision like image captioning, retrieval, and generation. Students will learn popular CNN architectures, train models using TensorFlow and Keras, and develop real-world computer vision applications. Hands-on exercises and case studies are included to provide practical experience with computer vision tasks.
Mini Project- Face Recognition
Mini Project- Face RecognitionUniversity of Hertfordshire, School of Electronic Communications and Electrical Engineering
The following resources come from the 2009/10 BSc (Hons) in Multimedia Technology (course number 2ELE0075) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objectives of this project are to demonstrate abilities to:
• Handle camera setup, calibrate and capture still and video faces
• Pre-process images and extract features
• Perform face recognition by a) using existing methods and b) trying new techniques.
This project requires the students to apply their abilities to handle image capture hardware and software. Since this is an active area of research, students will need to perform literature survey and discuss ( through brainstorm sessions) their performance characteristics. In addition, they will need to design and implement pre-processing and recognition codes leading to face recognition.Similar to “Tools and Strategies for Quickly Building Effective Image Datasets,” a Presentation from BDTI (20)
Real Time Moving Object Detection for Day-Night Surveillance using AI
Real Time Moving Object Detection for Day-Night Surveillance using AI
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
A Ensemble Learning-based No Reference QoE Model for User Generated Contents
A Ensemble Learning-based No Reference QoE Model for User Generated Contents
A survey on Measurement of Objective Video Quality in Social Cloud using Mach...
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Deep Learning Projects - Anomaly Detection Using Deep Learning
Deep Learning Projects - Anomaly Detection Using Deep Learning
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...
“Applying the Right Deep Learning Model with the Right Data for Your Applicat...
“Practical Image Data Augmentation Methods for Training Deep Learning Object ...
“Practical Image Data Augmentation Methods for Training Deep Learning Object ...
USING IMAGE CLASSIFICATION TO INCENTIVIZE RECYCLING
USING IMAGE CLASSIFICATION TO INCENTIVIZE RECYCLING
Kyligence Cloud 4 - Feature Focus: AI-Augmented Engine
Kyligence Cloud 4 - Feature Focus: AI-Augmented Engine
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“Tools and Strategies for Quickly Building Effective Image Datasets,” a Presentation from BDTI
- 1. © 2021 EJ Technology Consultants Tools and Strategies for Quickly Building Effective Image Datasets Evan Juras Computer Vision Engineer
- 2. © 2021 EJ Technology Consultants Purpose • Demonstrate practical strategies and tools for quickly building image datasets • Train higher-quality models with less time investment • Focus on object detection models 2 mAP % mAP %
- 3. © 2021 EJ Technology Consultants Object Detection Neural Networks • Object detection models locate and identify objects in images • Use CNNs to find features in images and correlate them to known objects they’ve been trained on • For this presentation, “training” is transfer learning and fine-tuning 3 Visualizing and Comparing Convolutional Neural Networks arXiv: 1412.6631v2
- 4. © 2021 EJ Technology Consultants Applying Object Detection Models • Scope of application determines amount of training images needed • Constrained applications with limited variety of visual conditions: • Lower generalization of model is okay • Fewer training images needed • Applications with wide variety of visual conditions: • Need to be accurate at different light levels, angles, coloration, distance from camera, etc. • Larger number of images are needed 4 Playing card detector – low variance Self-driving car – high variance
- 5. © 2021 EJ Technology Consultants How can we Quickly Create an Object Detection Dataset? • Problem: to train a new object detection model, a large image dataset is needed • Manually gathering and labeling images is time consuming 5 • Solution: Use these strategies to accelerate dataset creation! • Using datasets already available online • Capturing images from video • Using sleek annotation and automated methods tools to quickly label data • Synthetic image generation and data augmentation My example – Bison distance detector
- 6. © 2021 EJ Technology Consultants Using Online Image Datasets • Datasets available online can be a good starting point • Academic datasets such as Open Images Dataset, ImageNet, COCO, etc. • User-contributed datasets from TensorFlow, Kaggle, or other sources • Issues with online datasets • Can be filled with poor quality images or label data • Datasets not be available or appropriate for your application • Copyright and licensing: need to be careful using copyrighted material for commercial purposes 6
- 7. © 2021 EJ Technology Consultants Capturing Images from Video • Gathering images from the camera used by the application significantly improves accuracy 7 • Various methods for capturing images specific to your application: • Set up cameras in situations similar to actual in-situ application, record video, and grab frames from video • Use multiple cameras in multiple locations and aggregate videos to a central server • Use online video or live camera streams of objects you are interested in
- 8. © 2021 EJ Technology Consultants Capturing Images from Video, Continued Python script to extract individual frames from a video: github.com/EdjeElectronics/Image-Dataset-Tools#FrameGrabber 8
- 9. © 2021 EJ Technology Consultants Image Annotation Tools • Many annotation tools are available to help accelerate the image labeling process • Paid annotation tools: • V7 Darwin • Supervisely • Hive – pay humans to annotate your data • Lionbridge – pay humans to annotate your data • Free annotation tools: • CVAT • LabelImg 9 Video annotation tool from V7
- 10. © 2021 EJ Technology Consultants Automated Labeling Methods • Supervised automated labeling • Train a model off a small portion of the dataset • Use that model to automatically label the rest of the dataset, while manually rejecting poor labels and re-labeling them • github.com/EdjeElectronics/Image-Dataset- Tools#AutoLabeler • Other creative methods • Tryolabs example uses OpenPose to automatically locate positions of heads in a frame 10 Face mask detection in street camera video streams using AI: behind the curtain (Courtesy of Tryolabs) Automatically labeling raccoon images
- 11. © 2021 EJ Technology Consultants Synthetic Image Generation and Data Augmentation 11 Synthetic Image Generation Data Augmentation • Tools are available to synthetically generate images • Mindtech • KineticVision Synthetic scenario video from Mindtech • Generate new images from existing ones • Increase visual variety of images • Resolve class imbalances • Great for 2D objects and perspectives Check out my data augmentation talk from EVS2020!
- 12. © 2021 EJ Technology Consultants Limitations of These Methods • Still need some manual groundwork involved in collecting images • Searching for datasets, setting up video recording, auditing labels for accuracy • Other limitations • Difficult to use for uncommon objects or applications • Won’t bring model up to highest accuracy possible – for best performance, need to carefully curate and refine dataset, which takes time 12
- 13. © 2021 EJ Technology Consultants Conclusions • There are various methods for quickly building an object detection dataset • Online datasets • Fast image capture • Sleek annotation tools and automated labeling • Synthetic image generation and data augmentation • Depending on application, may still need to manually curate dataset for best accuracy • Same concepts can be applied to image classification models 13
- 14. © 2021 EJ Technology Consultants Example of Resource Slide 14 Resource Links Browser-based free annotation tool (CVAT): https://cvat.org Using TensorFlow’s built-in datasets: https://www.tensorflow.org/datasets/overview Handy scripts for working with image datasets: https://github.com/EdjeElectronics/Image-Dataset-Tools Contact Information Website: www.ejtech.io Email: evan.juras@ejtech.io References [1]: M. Zeiler, R. Fergus (2013). Visualizing and Understanding Convolutional Networks. arXiv:1311.2901 [2]: W. Yu, K. Yang, Y. Bai, H. Yao, Y. Rui (2014). Visualizing and Comparing Convolutional Neural Networks. arXiv:1412.6631v2