EasyClassify
Deep Learning Classification Library
- Includes functions for classifier training and image classification
- Detects defective products
- Sorts products into various classes
- Supports data augmentation
- Compatible with CPU and GPU processing
- Deep Learning Studio for dataset creation, training and evaluation
- Available as part of the Deep Learning Bundle
- Also as cost effective inference-only license
Description
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.
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.
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
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.
Out Of Distribution Detection
Out of Distribution Detection (OOD) is a feature of EasyClassify that identifies images that are significantly different from the data the model was trained on, and thus, are likely to be misclassified. OOD is an important feature to build robust and reliable deep learning classification systems for real-world applications.
Cost-Effective Inference License
Usually , deploying Deep Learning on the production floor only requires inference processing. Inference is the process of using a previously trained model to inspect, analyze newly acquired images. The training is, in most contexts, an offline process. Training can be executed using the Open eVision API and requires a license of the Deep Learning Bundle. Alternatively, training can also be performed, free of charge, with the Deep Learning Studio application. Inference-only licenses are an alternative to the Deep Learning Bundle license, allowing the customer to deploy cost-optimized deep learning solutions.
Other Benefits
Neo Licensing System
Neo is the new Licensing System. It is reliable, state-of-the-art, and is now available to store Open eVision and eGrabber licenses. Neo allows you to choose where to activate your licenses, either on a Neo Dongle or in a Neo Software Container. You buy a license, you decide later.
Neo Dongles offer a sturdy hardware and provide the flexibility to be transferred from a computer to another. Neo Software Containers do not need any dedicated hardware, and instead are linked to the computer on which they have been activated.
Neo ships with its own, dedicated Neo License Manager which comes in two flavours: an intuitive, easy to use, Graphical User Interface and a Command Line Interface that allows for easy automation of Neo licensing procedures.
All eVision Libraries For Windows And Linux
- Microsoft Windows 11, 10 for x86-64 (64-bit) processor architecture
- Microsoft Windows 11, 10 IoT Enterprise on x86_64 systems
- Linux for x86-64 (64-bit) and ARMv8-A (64-bit) processor architectures with a glibc version greater or equal to 2.18
Open eVision 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 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.
