Search Results for "image analysis algorithm"
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Showing 15 open source projects for "image analysis algorithm"
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Clair
Vulnerability Static Analysis for Containers
Clair is an application for parsing image contents and reporting vulnerabilities affecting the contents. This is done via static analysis and not at runtime. Clair v4 utilizes the ClairCore library as its engine for examining contents and reporting vulnerabilities. At a high level you can consider Clair a service wrapper to the functionality provided in the ClairCore library. -
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ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that... -
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SageMaker Spark Container
Docker image used to run data processing workloads
Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. ... -
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. ... -
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KOTS
KOTS provides the framework, tools and integrations
...The Kots CLI also serves as the bootstrapper for the in-cluster Kubernetes application Admin Console kotsadm which can be used to automate the core Kots CLI tasks for managing applications (license verification, configuration, updates, image renaming, version controlling changes, and deployment) as well as additional KOTS tasks (running preflight checks and performing support bundle analysis). The install command is the recommended way to learn KOTS. Executing the install command will install an application and the kotsadm Admin Console to an existing Kubernetes cluster. ... -
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MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity... -
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solgraph
Visualize Solidity control flow for smart contract security analysis
Visualize Solidity control flow for smart contract security analysis. Generates a DOT graph that visualizes the function control flow of a Solidity contract and highlights potential security vulnerabilities. A nice example of piping contract source that is in your clipboard through solgraph, dot, and preview: (Use whatever image previewer is available on your system; Preview.app is available on Mac) You have to have graphviz installed (brew install graphviz) to render the DOT file as an image. ... -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
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Oyente
An Analysis Tool for Smart Contracts
A container with the required dependencies configured can be found here. The image is however outdated. We are working on pushing the latest image to dockerhub for your convenience. If you experience any issues with this image, please try to build a new docker image by pulling this codebase before open an issue. A collection of the utilities that were developed for the paper are in misc_utils. Use them at your own risk - they have mostly been disposable. This is an improved version of the... -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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SPADE
A toolkit for developing and deploying protein structure algorithms.
The Structural Proteomics Application Development Environment is a Python tool kit for developing and deploying bioinformatics applications. Handles graphics, analysis, and modeling of protein sequence and structure. Source and Win installers available. SPADE source code can be cloned from http://www.github.com/deaconjs/SPADE. -
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages. -
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PHP Business Rules is an open source application layer for PHP. The logic is defined in rules and facts. Like most Business Rule Engines (BRE) it uses the Rete algorithm.
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G.A.V. (Graph Algorithm Visualizer) is a tool that visualizes algorithms from graph theory. A step-by-step visualization from each different algorithm allows the user to understand the particular algorithm very easily.
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Theba is a plugin-based image analysis framework for segmentation of and measurements on 3D and 2D images. Theba has a nice GUI that allows inspection and manipulation of the image and a wide range of plugins including segmentation.
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