Deep Learning segmentation library
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12/18/2023 Datasheet Deep Learning segmentation library /--/uploads/2023/11/EasySegment.png)
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EasySegment is the segmentation tool of Deep Learning Bundle. EasySegment performs defect detection and segmentation. It identifies parts that contain defects, and precisely pinpoints where they are in the image.
– The supervised mode of EasySegment works by learning a model of what is a defect and what is a “good” part in an image. This is done by training with images annotated with the expected segmentation. Then, the tool can be used to detect and segment the defects in new images.
The supervised mode of EasySegment achieves better precision and can segment more complex defects than the unsupervised mode thanks to the knowledge of the expected segmentation.
– The unsupervised mode of EasySegment works by learning a model of what is a “good” sample (i.e. a sample without any defect). This is done by training it only with images of “good” samples.
Then, the tool can be used to classify new images as good or defective and segment the defects from these images. By training only with images of good samples, the unsupervised mode of EasySegment is able to perform inspection even when the type of defect is not known beforehand or when defective samples are not readily available.
EasyClassify, EasySegment and EasyLocate can be purchased as part of the Deep Learning bundle or individually as inference only licenses.
DownloadOpen 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 EasySegment, Deep Learning Studio integrates an annotation tool and can transform prediction into ground truth annotation. It also 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.
Open eVision is a set of 64-bit libraries that require an Intel compatible processor with the SSE4 instruction set or an ARMv8-A compatible processor.
Open eVision can be used on the following operating systems:
Microsoft Windows 11, 10, 8.1, 7 for x86-64 (64-bit) processor architecture
Linux for x86-64 (64-bit) and ARMv8-A (64-bit) processor architectures with a glibc version greater or equal to 2.18
Remote connections
Remote connections are allowed using remote desktop, TeamViewer or any other similar software.
Virtual machines
Virtual machines are supported. Microsoft Hyper-V, Oracle VirtualBox and libvirt hypervisors have been successfully tested.
Only the Neo Licensing System is compatible with virtualization.
Minimum requirements:
2 GB RAM to run an Open eVision application
8 GB RAM to compile an Open eVision application
Between 100 MB and 2 GB free hard disk space for libraries, depending on selected options.
Supported programming languages :
The Open eVision libraries and tools support C++, Python and the programming languages compatible with the .NET Framework (C#, VB.NET)
C++ requirements: A compiler compatible with the C++ 11 standard is required to use Open eVision
Python requirements: Python 3.11 or later is required to use the Python bindings for Open eVision
.NET requirements: .NET Framework versions 4.8 or later are supported
Supported Integrated Development Environments:
Microsoft Visual Studio 2017 (C++, C#, VB .NET, C++/CLI)
Microsoft Visual Studio 2019 (C++, C#, VB .NET, C++/CLI)
Microsoft Visual Studio 2022 (C++, C#, VB .NET, C++/CLI)
QtCreator 4.15 with Qt 5.12
PC4188 Open EasySegment for USB dongle
PC4338 Open eVision EasySegment
PC4193 Open EasySegment Inference for USB dongle
PC4343 Open eVision EasySegment Inference
PC4182 Open Deep Learning Bundle for USB dongle
PC4332 Open eVision Deep Learning Bundle
Please use Open eVision instead for new developements.
In order to be able to work on your project, we need as much information as possible about what you are trying to achieve. What are your requirements in terms of accuracy and processing time? Which platform (CPU / GPU, Windows / Linux, Intel / ARM) do you plan to use? How are your images captured? How are they annotated / labelled, etc.