Showing 4 open source projects for "parameter estimation"

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  • 1
    Qwen-Image

    Qwen-Image

    Qwen-Image is a powerful image generation foundation model

    Qwen-Image is a powerful 20-billion parameter foundation model designed for advanced image generation and precise editing, with a particular strength in complex text rendering across diverse languages, especially Chinese. Built on the MMDiT architecture, it achieves remarkable fidelity in integrating text seamlessly into images while preserving typographic details and layout coherence.
    Downloads: 7 This Week
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  • 2
    pattern_classification

    pattern_classification

    A collection of tutorials and examples for solving machine learning

    The pattern_classification repository is an educational project that provides tutorials, examples, and reference materials related to machine learning and statistical pattern recognition. The project aims to help learners understand the process of building predictive models by presenting structured explanations and practical examples. It includes notebooks and guides that demonstrate data preprocessing, feature extraction, model training, and evaluation techniques used in machine learning...
    Downloads: 0 This Week
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  • 3
    ConvNet Burden

    ConvNet Burden

    Memory consumption and FLOP count estimates for convnets

    ...It lets users compute approximate burdens (in FLOPs, memory) for standard image classification CNN models (e.g. ResNet, VGG) based on network definitions. The tool helps researchers compare the computational efficiency of architectures or quantify resource needs. Estimation of memory consumption (e.g. feature map sizes, parameter storage). Support for multiple network definitions/architectures. Estimation of memory consumption (e.g. feature map sizes, parameter storage). Estimation of FLOPs (floating point operations) for CNN architectures.
    Downloads: 0 This Week
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  • 4
    PhiWeave is a machine learning library for structured prediction via factor graphs. It is part of an ongoing effort to implement and improve on the current state-of-the-art in inference and parameter estimation for graphical models.
    Downloads: 0 This Week
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