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EasyClassify

Deep Learning classification library

At a glance
  • Includes functions for classifier training and image classification
  • Able to detect defective products or sort products into various classes
  • Supports data augmentation, works with as few as one hundred training images per class
  • Compatible with CPU and GPU processing
  • Includes the free Deep Learning Studio application for dataset creation, training and evaluation
  • Only available as part of the Deep Learning Bundle

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What Is Deep Learning ?

Neural Networks are computing systems inspired by the biological neural networks that constitute the human brain. Convolutional Neural Networks (CNN) are a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing images. Deep Learning uses large CNNs to solve complex problems difficult or impossible to solve with so-called conventional computer vision algorithms. Deep Learning algorithms may be easier to use as they typically learn by example. They do not require the user to figure out how to classify or inspect parts. Instead, in an initial training phase, they learn just by being shown many images of the parts to be inspected. After successful training, they can be used to classify parts, or detect and segment defects.


Data Augmentation
Data Augmentation

Deep Learning works by training a neural network, teaching it how to classify a set of reference images. The performance of the process highly depends on how representative and extensive the set of reference images is. Deep Learning Bundle implements “data augmentation”, which creates additional reference images by modifying (for example by shifting, rotating, scaling) existing reference images within programmable limits. This allows Deep Learning Bundle to work with as few as one hundred training images per class.


Performance
Performance

Deep Learning generally requires significant amounts of processing power, especially during the learning phase. Deep Learning Bundle supports standard CPUs and automatically detects Nvidia CUDA-compatible GPUs in the PC. Using a single GPU typically accelerates the learning and the processing phases by a factor of 100.


Classification
Classification

EasyClassify is the classification tool of Deep Learning Bundle. EasyClassify requires the user to label training images, that is to tell which ones are good and which ones are bad, or which ones belong to which class. After this learning/training process, the EasyClassify library is able to classify images. For any given image, it returns a list of probabilities, showing the likelihood that the image belongs to each of the classes it has been taught. For example, if the process requires setting apart bad parts from good ones, EasyClassify returns whether each part is good or bad, and with what probability.


Why Choose Open eVision’s Deep Learning Bundle?
Why Choose Open eVision’s Deep Learning Bundle?

  • Deep Learning Bundle has been tailored, parametrized and optimized for analyzing images, particularly for machine vision applications.
  • Deep Learning Bundle has a simple API and the user can benefit from the power of deep learning technologies with only a few lines of code.
  • Try before you buy: Deep Learning Bundle comes with the free Deep Learning Studio training and evaluation application.
EasyClassify and EasySegment cannot be purchased separately. They are only available as part of the Deep Learning Bundle.
Download and evaluate Deep Learning Bundle using Deep Learning Studio today, and feel free to call Euresys’ support should you have any question.


Developed with the support of the DG06 Technology Development Department
Developed with the support of the DG06 Technology Development Department


What is EasyClassify good for?
What is EasyClassify good for?

Deep Learning is generally not suitable for applications requiring precise measurement or gauging. It is also not recommended when some types of errors (such as false negative) are completely unacceptable. EasyClassify performs better than traditional machine vision when the defects are difficult to specify explicitly, for example, when the classification depends on complex shapes and textures at various scales and positions. Besides, the "learn by example" paradigm of Deep Learning can also reduce the development time of a computer vision process.


Deep Learning Studio
Deep Learning Studio

Open eVision includes the free Deep Learning Studio application. This application assists the user during the creation of the dataset as well as the training and testing of the deep learning tool. For the unsupervised mode of EasySegment, Deep Learning Studio allows to graphically configure the tool to fit performance requirements. For example, after training, one can choose a tradeoff between a better defect detection rate or a better good detection rate.


Software
Host PC Operating System
  • Open eVision is a set of 32-bit and 64-bit libraries that require a processor compatible with the SSE2 instruction set.
  • Deep Learning Bundle is only available in the 64-bit Open eVision library.
  • Open eVision can be used on the following operating systems:
    • Windows 10 (32- and 64-bits)
    • Windows 8 (32- and 64-bits)
    • Windows 7 (32- and 64-bits)
  • Since Open eVision 2.6, discontinued support of:
    • Windows Vista 32-bits Service Pack 1
    • Windows XP 32-bits Service Pack 3
    • Windows Embedded Standard 2009 32-bits
  • The Open eVision installer does not allow installation on virtual machines.
  • Minimum requirements:
    • RAM: 8 GB
    • Display size: 800 x 600. 1280 x 1024 recommended.
    • Color depth: 16 bits. 32 bits recommended.
    • Between 100 MB and 2 GB free hard disk space for libraries, depending on selected options.
APIs
  • Supported Integrated Development Environments and Programming Languages:
    • Microsoft Visual Studio .NET 2003 SP1 (C++)
    • Microsoft Visual Studio 2005 SP1 (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2008 SP1 (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2010 (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2012 (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2013 (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2015 (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2017 (C++, C#, VB .NET, C++/CLI)
  • Since Open eVision 2.6, discontinued support of:
    • Microsoft Visual Studio 6.0 SP6 (C++, Basic)
    • Borland C++ Builder 6.0 update 4 (C++)
    • CodeGear C++ Builder 2009 (C++)
    • CodeGear Delphi 2009 (Object Pascal)
    • Embarcadero RAD Studio XE4 (C++, Object Pascal)
    • Embarcadero RAD Studio XE5 (C++, Object Pascal)
Ordering Information
Product code - Description
Presence Check

Presence / Absence check

EasyImage gray-scale analysis functions are used for simple presence/absence checks
Surface

Surface analysis

EasyImage is used to reveal the surface defects, and the blob analysis functions of EasyObject is able to segment and measure them.
Code Verification

Code quality verification for label printing machines