PIRM-SR challenge

PSNR Calculation MATLAB Code for PIRM-SR Challenge The PIRM-SR challenge aims to compare and rank perceptual single-image super-resolution methods. In terms of perceptual quality, state-of-the-art methods often perform poorly when evaluated with 'simple' distortion metrics like PSNR and SSIM. Hence, in contrast to previous challenges, the evaluation and ranking will focus on perceptual quality, adopting a unified approach that combines algorithm accuracy with perceptual quality. This allows perceptual-driven methods to compete with those designed to maximize PSNR. To self-verify your method, use this MATLAB code to compute RMSE and perceptual scores for your output on a self-validation set. Here's how to quickly get started: Copy your output to the your_results folder in the base directory. Copy only the HR images to the self_validation_HR folder. Download and extract the SR-Metric toolbox into the utils/sr-metric-master folder. Run the evaluate_results.m script. Troubleshooting Depending on your operating system, you might need to recompile the MEX files in the matlabPyrTools toolbox. If that's the case, follow these steps:- Navigate to utils/sr-metric-master/external/matlabPyrTo and recompile the MEX files.

二抽取代码MATLAB面向PhysioNet / CinC Challenge 2020的示例预测代码包含两个主要脚本来训练模型和对数据进行分类：train_model.m训练模型，您需要将模型代码添加到train_12ECG_model函数。此脚本执行所有文件的输入和输出，请勿编辑。driver.m是分类器，从train_model调用输出并执行文件输入输出，请勿编辑。请检查这些文件中的代码以获取输入输出格式。要创建和保存模型，编辑train_12ECG_classifier.m脚本，注意不要更改函数的输入参数或添加输出参数。所需模型和参数应保存在单独的文件中。示例中附加脚本get_12ECG_features.m用于提取特征。运行分类器需编辑run_12ECG_classifier.m脚本，该脚本以单个记录作为输入并输出预测结果。

This repository contains 94 solutions to solve Fibonacci problems on MATLAB Cody. The solutions are ranked from the most common to the least common, indicating increasing complexity. The solutions are correct and come with comments, but they may not be the most efficient. Below are some examples of related problems and their solution counts: Fibonacci sequence: 7,859 solvers Create a times table: 6,488 solvers Sum all numbers in an input vector: 23,225 solvers Identify whether a number is odd: 16,120 solvers Note: While the solutions for the Fibonacci sequence are effective, they may not be optimized for performance. Thank you for the useful resources and links provided for inspiration! License: Free to use and distribute.

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熊猫作业-深入Python Pandas数据背景！现在是时候了解您的Pandas技能并将其应用于新挑战。您需要完成Pandas Challenge中的一项任务。确保专注并将您的技能发挥到极致，因为这些技能将成为您数据分析工具箱中的强大资产。创建名为pandas-challenge的新存储库，并在本地设置HeroesOfPymoli的Jupyter笔记本以运行分析。完成后将更改推送到GitHub或GitLab。

这是Deep SR-ITM（ICCV 2019）的官方存储库，提供训练、测试代码及训练好的权重和数据集（训练+测试）。我们的论文在ICCV 2019上接受了口头报告，详细介绍了Deep SR-ITM的超分辨率和反色调映射联合学习应用于4K UHD HDR的情况。我们的代码使用MatConvNet实现，需要MATLAB环境，并已在MATLAB 2017a、MatConvNet 1.0-beta25、CUDA 9.0、10.0、cuDNN 7.1.4及NVIDIA TITAN Xp GPU下进行了测试。请参考以下详细安装说明并下载源代码到您的目录。

Microsoft SQL Server 测试数据库示例 AdventureWorks2008R2_SR1。

档深入探讨了 spring-cloud-stream-app-descriptor-Celsius.SR3.stream-apps-kafka-10-docker 的技术细节，分析其在 Spring Cloud Stream 生态系统中的作用和应用。

微软官方的SQL Server示例数据库AdventureWorks，现在在官网已经不再提供AdventureWorks2008R2_SR1.exe的离线下载。建议大家尽快保存该资源，以备不时之需。

通过Pytorch重新实现的Deep-SR-ITM，该模型结合超分辨率和逆色调映射，特别设计用于4K UHD HDR应用。研究团队金秀艺、吴志亨和金曼彻尔在IEEE计算机视觉国际会议上进行了口头报告。代码基于原Matlab版本转换而来，保留了原始设置，重点不在改进基线，而在于提供不同实现的选择。测试环境包括Ubuntu 16.04 LTS，Python 3.7.5，Pytorch 1.3.1，TorchVision 0.4.2，CUDA 10.1，OpenCV 3.4.2。数据准备阶段包括从.mat格式转换为'.png'格式，适用于SDR和HDR图像。如需更多细节，请参阅原始repo。