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Type: User
Bio: student
Type: User
Bio: student
[ECCV 2018] CCPD: a diverse and well-annotated dataset for license plate detection and recognition
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
paper data process
李宏毅《机器学习》笔记,在线阅读地址:https://datawhalechina.github.io/leeml-notes
基于Yolov5车牌检测,更快更准.
A two stage lightweight and high performance license plate recognition in MTCNN and LPRNet
该工作由电子科技大学陈昂、刘俊凯、夏子寒同学完成。我们提出可以使用深度学习的方案对**车牌进行检测和识别。我们提出使用YOLOv3网络进行车牌检测,然后创新性地对检测后的车牌使用空间变换网络STN加以校正,最后使用LPRNet网络进行车牌的字符与数字识别。目前实测结果,在老师提供的45张数据集上,我们的YOLOv3网络检测准确率(IOU)达到98.2%,深度学习级联网络识别准确率为95.6%。我们采用大量测试集,最终我们的YOLOv3_STN_LPRNet级联网络识别准确率稳定在93.3%,不加空间变换网络STN的话,识别准确率在66%左右。 总体来说,使用深度学习的方案比传统方案效果提升的非常好,而且我们加入空间变换网络STN的做法对于提升识别准确率很有效。
Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.
A repository for temporary code, in order to review on my ipad on the platform of Github.
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
分别使用OpenCV、ONNXRuntime部署yolov5检测车牌和4个角点,包含C++和Python两个版本的程序
download from csdn
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.