Angela  Dickens

Angela Dickens


Data Augmentation in YOLOv4

The object detection space continues to move quickly. No more than two months ago, the Google Brain team released EfficientDet for object detection, challenging YOLOv3 as the premier model for (near) realtime object detection, and pushing the boundaries of what is possible in object detection. We wrote a series of posts comparing YOLOv3 and EfficientDettraining YOLOv3 on custom dataand training EfficientDet on custom data, and we’ve found impressive results.

**See here for our tutorial on how to train YOLOv4 on your custom dataset.

And now YOLOv4 has been released showing an increase in COCO Average Precision (AP) and Frames Per Second (FPS) by 10 percent and 12 percent, respectively. In this post, we will see how the authors made this breakthrough by diving into the specifics of the data augmentation techniques used in YOLOv4.

The founder of Mosaic Augmentation, Glen Jocher has released a new YOLO training framework titled YOLOv5\. You may also want to see our post on YOLOv5 vs YOLOv4 This post will explain some of the pros of the new YOLOv5 framework.

YOLOv5 Breakdown


The importance of data augmentation for computer vision is not new! See our post from January explaining how important image preprocessing and augmentation is for computer vision.

What is the Bag of Freebies in YOLOv4?

The authors of YOLOv4 include a series of contributions in their paper titled a “bag of freebies.” These are a series of steps that can be taken to improve the model’s performance without increasing latency at inference time. Because they cannot affect the model’s inference time, most of these make improvements in the data management and data augmentation of the training pipeline. These techniques improve and scale up the training set to expose the model to scenarios that would have otherwise been unseen. Data augmentation in computer vision is key to getting the most out of your dataset, and state of the art research continues to validate this assumption.

Data Augmentation in Computer Vision

Image augmentation creates new training examples out of existing training data. It’s impossible to truly capture an image for every real-world scenario our model may be tasked to see in inference. Thus, adjusting existing training data to generalize to other situations allows the model to learn from a wider array of situations.

The authors of YOLOv4 cite a number of techniques that ultimately inspired the inclusion of their bag of freebies. We provide an overview below.


**Photometric Distortion — **This includes changing the brightness, contrast, saturation, and noise in an image. (For example, written on blur data augmentation in computer vision.)

Adjusting brightness on our platform

**Geometric Distortion — **This includes random scaling, cropping, flipping, and rotating. These types of augmentation can be particularly tricky as the bounding boxes are also affected and must be updated. (As an example, we’ve written on how to use random cropping data augmentation in computer vision.)

Flipping images on our platform

Those two methods were both pixel adjustments, meaning that the original image could easily be recovered with a series of transformations.

Image Occlusion

**Random Erase — **This is a data augmentation technique that replaces regions of the image with random values, or the mean pixel value of training set. Typically, it is implemented with varying proportion of image erased and aspect ratio of erased area. Functionally, this becomes a regularization technique, which prevents our model from memorizing the training data and overfitting.

#data-augmentation #data-preprocessing #object-detection #computer-vision #data-preparation #data analysis

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Data Augmentation in YOLOv4
 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Uriah  Dietrich

Uriah Dietrich


What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data