Coaxpress Frame Grabbers
Camera Link Frame Grabbers
Non-Standard AnalogFrame Grabbers
Standard PAL/NTSCvideo capture cards
HD-SDI/HDMIvideo encoders
Video converters
GigE Vision, USB3 Vision, CoaXPress
Image AnalysisSoftware Tools
Evaluation andprototyping tool
Convolutional Neural Network-based classification library
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. EasyDeepLearning 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 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. EasyDeepLearning implements “data augmentation”, which creates additional reference images by modifying (for example by shifting, rotating, scaling) existing reference images within programmable limits. This allows EasyDeepLearning to work with as few as one hundred training images per class.
Open source versions of neural networks are available for free, so why choose Open eVision’s EasyDeepLearning?
Open eVision also includes the EasyDeepLearning Studio application. This application assists the user during the learning and testing phases.
Deep Learning generally requires significant amounts of processing power, especially during the learning phase. EasyDeepLearning 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.
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