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A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs. It was developed in 2015 for image recognition , and won the ImageNet Large Scale Visual Recognition Challenge ( ILSVRC ) of that year.
Self-contained DNN Model Pre-processing and Post-processing Run-time configuration for tuning & calibration DNN model interconnect Common platform TensorFlow, Keras, Caffe, Torch: Algorithm training No No / Separate files in most formats No No No Yes ONNX: Algorithm training Yes No / Separate files in most formats No No No Yes
Google Test UI is a software tool for testing computer programs, and serves as a test runner. It employs a 'test binary', a compiled program responsible for executing tests and analyzing their results, to evaluate software functionality. It visually presents the testing progress through a progress bar and displays a list of identified issues or ...
Data-driven testing: Data-driven testing with TestComplete means using a single test to verify many different test cases by driving the test with input and expected values from an external data source instead of using the same hard-coded values each time the test runs. COM-based, Open Architecture: TestComplete's engine is based on an open API ...
Model-based testing is an application of model-based design for designing and optionally also executing artifacts to perform software testing or system testing. Models can be used to represent the desired behavior of a system under test (SUT), or to represent testing strategies and a test environment.
Tricentis Tosca is a software testing tool that is used to automate end-to-end testing for software applications.It is developed by Tricentis.. Tricentis Tosca combines multiple aspects of software testing (test case design, test automation, test data design and generation, and analytics) to test GUIs and APIs from a business perspective. [1]
Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern
Capella was created by Thales in 2007, and has been under continuous development and evolution since then. The objective is to contribute to the transformation of engineering, providing an engineering environment which approach is based on models rather than focused on documents, piloted by a process, and offering, by construction, ways to ensure effective co-engineering.