LPRNet-Pytorch-master.zip

# LPRNet_Pytorch Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework. 完全适用于中国车牌识别（Chinese License Plate Recognition）及国外车牌识别！ 目前仅支持同时识别蓝牌和绿牌即新能源车牌等中国车牌，但可通过扩展训练数据或微调支持其他类型车牌及提高识别准确率！ # dependencies - pytorch >= 1.0.0 - opencv-python 3.x - python 3.x - imutils - Pillow - numpy # pretrained model * [pretrained_model](https://github.com/sirius-ai/LPRNet_Pytorch/tree/master/weights/) # training and testing 1. prepare your datasets, image size must be 94x24. 2. base on your datsets path modify the scripts its hyperparameters --train_img_dirs or --test_img_dirs. 3. adjust other hyperparameters if need. 4. run 'python train_LPRNet.py' or 'python test_LPRNet.py'. 5. if want to show testing result, add '--show true' or '--show 1' to run command. # performance - personal test datasets. - include blue/green license plate. - images are very widely. - total test images number is 27320. | size | personal test imgs(%) | inference@gtx 1060(ms) | | ------ | --------------------- | ---------------------- | | 1.7M | 96.0+ | 0.5- | # References 1. [LPRNet: License Plate Recognition via Deep Neural Networks](https://arxiv.org/abs/1806.10447v1) 2. [PyTorch中文文档](https://pytorch-cn.readthedocs.io/zh/latest/) # postscript If you found this useful, please give me a star, thanks!