Showing 8 open source projects for "nut-windows"

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  • 1
    Large Language Models (LLMs)

    Large Language Models (LLMs)

    Connect MATLAB to LLM APIs, including OpenAI® Chat Completions

    This repository enables MATLAB to connect with large language models (LLMs) such as OpenAI's ChatGPT, DALL-E, Azure OpenAI, and Ollama, integrating their natural language processing and image generation capabilities directly within MATLAB environments. It facilitates creating chatbots, summarizing text, and image generation, among other tasks.
    Downloads: 5 This Week
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  • 2
    MATLAB Deep Learning Model Hub

    MATLAB Deep Learning Model Hub

    Discover pretrained models for deep learning in MATLAB

    Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score.
    Downloads: 1 This Week
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  • 3
    Exposure Correction

    Exposure Correction

    Learning multi-scale deep model correcting over- and under- exposed

    Exposure_Correction is a research project that provides the implementation for the paper Learning Multi-Scale Photo Exposure Correction (CVPR 2021). The repository focuses on correcting poorly exposed photographs, handling both underexposure and overexposure using a deep learning approach. The method employs a multi-scale framework that learns to enhance images by adjusting exposure levels across different spatial resolutions. This allows the model to preserve fine details while correcting...
    Downloads: 1 This Week
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  • 4
    CometAnalyser

    CometAnalyser

    CometAnalyser, for quantitative comet assay analysis.

    ...Once the comets are segmented and classified, several intensity/morphological features are automatically exported as a spreadsheet file. Video Tutorial: CometAnalyser is written in MATLAB. It works with Windows, Macintosh, and UNIX-based systems. Please, download the sample datasets and test it watching the video tutorial to understand how it works: https://www.youtube.com/watch?v=vh2VFnMw50A Contacts: filippo.piccinini85@gmail.com beleonattila@gmail.com
    Downloads: 25 This Week
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  • 5
    Spheroid_segmentation

    Spheroid_segmentation

    Deep learning networks for spheroid segmentation

    To accelerate the analysis of tumors' spheroids, different deep learning networks were trained to automatize the segmentation process. The code provides the trained networks based on Vgg16, Vgg19, ResNet18, and ResNet50 ready to be used for segmentation purposes. It also provides Matlab functions ready to be used to train new networks, segment new images, and measure the quality of the training using different quantitative parameters.
    Downloads: 0 This Week
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  • 6
    Robust Tube MPC

    Robust Tube MPC

    Example implementation for robust model predictive control using tube

    robust-tube-mpc is a MATLAB implementation of robust tube-based Model Predictive Control (MPC). The framework provides tools to design and simulate controllers that maintain stability and constraint satisfaction in the presence of bounded disturbances. Tube-based MPC achieves robustness by combining a nominal trajectory planner with an error feedback controller that keeps the actual system state within a "tube" around the nominal trajectory. This repository includes example scripts and...
    Downloads: 0 This Week
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  • 7
    MatlabFunc

    MatlabFunc

    Matlab codes for feature learning

    MatlabFunc is a collection of MATLAB functions developed by the ZJULearning group to support various tasks in computer vision, machine learning, and numerical computation. The repository brings together a wide range of utility scripts, algorithms, and implementations that serve as building blocks for research and development. These functions cover areas such as matrix operations, optimization, data processing, and visualization, making them broadly applicable across different research...
    Downloads: 3 This Week
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  • 8
    Detect and Track

    Detect and Track

    Code release for "Detect to Track and Track to Detect", ICCV 2017

    Detect-Track is the official implementation of the ICCV 2017 paper Detect to Track and Track to Detect by Christoph Feichtenhofer, Axel Pinz, and Andrew Zisserman. The framework unifies object detection and tracking into a single pipeline, allowing detection to support tracking and tracking to enhance detection performance. Built upon a modified version of R-FCN, the code provides implementations using backbone networks such as ResNet-50, ResNet-101, ResNeXt-101, and Inception-v4, with...
    Downloads: 1 This Week
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