cnn源码matlab-SVHN-deep-digit-detector:自然场景中的深度数字检测器(和识别器)。使用带有tensorflow

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cnn源码matlab SVHN-deep-cnn-digit-detector 该项目在自然场景中实现了 deep-cnn-detector(和识别器)。 我使用 keras 框架和 opencv 库来构建检测器。 该检测器使用 CNN 分类器为 MSER 算法提出的区域确定数字与否。 先决条件 Python 2.7 keras 1.2.2 opencv 2.4.11 张量流-GPU == 1.0.1 等等。 运行这个项目所需的所有包的列表可以在 . Python环境 我建议您创建和使用独立于您的项目的 anaconda 环境。 您可以按照以下简单步骤为该项目创建 anaconda env。 使用以下命令行创建 anaconda env: $ conda env create -f digit_detector.yml 激活环境$ source activate digit_detector 在这个环境中运行项目 用法 数字检测器的构建过程如下: 0. 下载数据集 下载 train.tar.gz 并解压文件。 1.加载训练样本(1_sample_loader.py) Svhn 以 m

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