--- title: lpsrgan_for_test canonical_url: "https://www.modelscope.cn/models/LittleWayneMS/lpsrgan_for_test" md_url: "https://www.modelscope.cn/models/LittleWayneMS/lpsrgan_for_test.md" repository: LittleWayneMS/lpsrgan_for_test last_updated: 2026-03-25 pipeline_tag: bad-image-detecting tasks: - bad-image-detecting library_name: - pytorch frameworks: - Pytorch downloads: 20 stars: 0 --- # lpsrgan_for_test > lpsrgan_for_test - LittleWayneMS 在 ModelScope 开源的模型。环境配置在requirement.txt文件里面是我自己补充的。 - **Repository**: LittleWayneMS/lpsrgan_for_test - **Tasks**: bad-image-detecting - **Downloads**: 20 - **Stars**: 0 - **Last updated**: 2026-03-25 Source: https://www.modelscope.cn/models/LittleWayneMS/lpsrgan_for_test --- # LPSRGAN: License Plate Super-Resolution GAN Unofficial implementation of the SR model proposed by Pan et al. in the paper "LPSRGAN: Generative Adversarial Networks for Super-Resolution of License Plate Image". This repository follows the methodology described by Nascimento et al. in "Toward Advancing License Plate Super-Resolution in Real-World Scenarios". ## Prerequisites - Python 3.8+ - PyTorch 1.10+ - CUDA 11.3+ (for GPU acceleration) - Required packages: `numpy`, `opencv-python`, `tqdm`, `yaml`, `torch` Install via: ```bash pip install -r requirements.txt ``` # Installation ```bash git clone https://github.com/your-username/LPSRGAN.git cd LPSRGAN ``` # Usage This section provides instructions on testing the model, training it from scratch, and fine-tuning it on a custom dataset. # Testing To test the model, ensure that the config file specifies the path to each of the .pth files (e.g., config file), as shown in the example below: ```yaml model: name: lpsrgan load: ./save/testing/model_lpsrgan.pth args: num_blocks: 3 dropout_prob: 0.5 model_ocr: name: GPLPR load: ./save/testing/model_ocr_GPLPR.pth args: alphabet: "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ" nc: 3 K: 7 isSeqModel: True head: 2 inner: 256 isl2Norm: True ``` Once the configuration is set, execute the following command to start the test: ``` python3 test_mv.py --config ./config/testing.yaml --save ./results ``` * Pretrained models are available at the following locations: - Download GP_LPR from [GP_LPR Unofficial Implementation](https://github.com/valfride/gplpr/tree/main). - The pre-trained model for LPSRGAN is available in the releases section of this repository. ## Training from Scratch To train the model from scratch, update the following variables in the [training config file](config/LPSRGAN/train_lpsrgan.yaml): ```yaml resume: null # Ensure no pretrained model is loaded train_dataset: dataset: args: path_split: ./custom_split.txt val_dataset: dataset: name: multi_image args: path_split: ./custom_split.txt phase: validation ``` Optionally, you can add the --tag argument for versioning: ```bash python3 train.py --config ./config/training.yaml --save path/to/save/dest --tag experiment_1 ``` ## Training on a Custom Dataset To train or fine-tune the model on a custom dataset, you need to create a .txt file that lists the image folder paths along with their corresponding data split (training, validation, or testing). Each line in the file should follow this format: ```txt ;; ``` Example: ```txt ABC1234;path/to/LP_image1.jpg;training XYZ5678;path/to/LP_image2.jpg;validation ``` Update training.yaml to point to your split file: ```yaml resume: path/to/lpsrgan.pth # Ensure the pretrained model is loaded train_dataset: dataset: args: path_split: ./custom_split.txt val_dataset: dataset: name: multi_image args: path_split: ./custom_split.txt phase: validation ``` For reference, you can check example files, such as [split_all_example.txt](split_all_example.txt) (with its images and .txt files annotations located under [dataset_intelbras_1280X960/mercosur](dataset_intelbras_1280X960/mercosur) and [dataset_intelbras_1920x1080](dataset_intelbras_1920x1080) directories), which demonstrate this format. ## Configuration Files * Training: config/training.yaml - Adjust hyperparameters, dataset paths, and optimizer settings here. * Testing: config/testing.yaml - Specify model checkpoints and test dataset details. ## Troubleshooting * CUDA Out of Memory: Reduce batch size in training.yaml. * Missing Pretrained Models: Ensure paths in testing.yaml or training.yaml are correct. * Dataset Errors: Validate split file formatting and image paths. ## Citation If you use this work, please cite the following works: ```bibtex @article{pan2024lpsrgan, title={LPSRGAN: Generative adversarial networks for super-resolution of license plate image}, author={Pan, Yuecheng and Tang, Jin and Tjahjadi, Tardi}, journal={Neurocomputing}, volume={580}, pages={127426}, year={2024}, publisher={Elsevier} } @article{nascimento2025toward, title = {Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark}, author = {V. {Nascimento} and G. E. {Lima} and R. O. {Ribeiro} and W. R. {Schwartz} and R. {Laroca} and D. {Menotti}}, year = {2025}, journal = {Journal of the Brazilian Computer Society}, volume = {}, number = {}, pages = {1-14}, doi = {}, issn = {}, } ``` Additionally, consider showing your support by **starring** :star: this repository. ## Related Publications Explore our other works on license plate recognition and super-resolution: - [Combining Attention Module and Pixel Shuffle for License Plate Super-resolution (SIBGRAPI 2022)](https://ieeexplore.ieee.org/document/9991753) - [Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers (Computers & Graphics 2023)](https://www.sciencedirect.com/science/article/pii/S0097849323000602?via%3Dihub) - [Full list of publications on vehicle identification](https://scholar.google.com/scholar?hl=pt-BR&as_sdt=0%2C5&as_ylo=2018&q=allintitle%3A+plate+OR+license+OR+vehicle+author%3A%22David+Menotti%22&btnG=) ## Contact For questions or feedback, contact: **Valfride Wallace do Nascimento** [[Webpage](https://www.inf.ufpr.br/vwnascimento/)] [vwnascimento@inf.ufpr.br](mailto:email@example.com)