Showing 8 open source projects for "batch"

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  • 1
    JCTools

    JCTools

    Java Concurrency Tools for the JVM

    Java Concurrency Tools for the JVM. This project aims to offer some concurrent data structures currently missing from the JDK. There’s more to come and contributions/suggestions are most welcome. JCTools has enjoyed support from the community and contributions in the form of issues/tests/documentation/code have helped it grow. JCTools offers excellent performance at a reasonable price (FREE! under the Apache 2.0 License). It’s stable and in use by such distinguished frameworks as Netty,...
    Downloads: 0 This Week
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  • 2
    Temporal Inference Engine

    Temporal Inference Engine

    A real time inference engine for temporal logical specifications

    A real time inference engine for temporal logical specifications, which is able to acquire, process and generate any binary or real signal through POSIX IPC, files or UNIX sockets. Specifications of signals and dynamic systems are represented as special graphs and executed in real time, with a predictable sampling time of few milliseconds. Real time signal processing, dynamic system control, state machine modeling and logical property verification are some fields of application of this...
    Downloads: 2 This Week
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  • 3
    Real-ESRGAN

    Real-ESRGAN

    Real-ESRGAN aims at developing Practical Algorithms

    ...The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. It emphasizes usability with utilities that handle alpha channels, gray/16-bit images, and tiled inference for large inputs, and can be run via Python scripts or portable executables.
    Downloads: 58 This Week
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  • 4
    DifferenceKit

    DifferenceKit

    A fast and flexible O(n) difference algorithm framework

    A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his...
    Downloads: 0 This Week
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    99.99% Uptime for MySQL and PostgreSQL on Google Cloud

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  • 5
    Detectron2

    Detectron2

    Next-generation platform for object detection and segmentation

    Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll...
    Downloads: 0 This Week
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  • 6
    AhoCorasickDoubleArrayTrie

    AhoCorasickDoubleArrayTrie

    An extremely fast implementation of Aho Corasick algorithm

    AhoCorasickDoubleArrayTrie is a Java implementation of the Aho–Corasick multi-pattern matching algorithm that is optimized using a Double-Array Trie data structure. It is designed for fast keyword scanning across large texts, where you want to search for many patterns simultaneously and efficiently. The core idea is to build an automaton from a dictionary of patterns, then stream through input text to emit matches with minimal overhead. By using a double-array trie representation, the...
    Downloads: 0 This Week
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  • 7
    Active Learning

    Active Learning

    Framework and examples for active learning with machine learning model

    ...The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks.
    Downloads: 3 This Week
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  • 8
    Free scripts & examples- JSP, Eclipse, SQL, PHP, ASP, Win32 Batch, Javascript, Flash/Actionscript. -=-=-=-=-=-=- NOTE: This project is closed. For new development related to integrating Beyond Compare with Eclipse, see http://beyondcvs.sourceforge.net/
    Downloads: 0 This Week
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