竞赛资料源码-第二届广州·琶洲算法大赛-智能交通CV模型赛题第2名方案.zip

English | [简体中文](README_CN.md) <div align="center"> <p align="center"> <img src="./docs/images/paddleseg_logo.png" align="middle" width = "500" /> </p> **A High-Efficient Development Toolkit for Image Segmentation Based on [PaddlePaddle](https://github.com/paddlepaddle/paddle).** [](LICENSE) [](https://github.com/PaddlePaddle/PaddleSeg/releases)    </div> <div align="center"> <img src="https://github.com/shiyutang/files/blob/9590ea6bfc36139982ce75b00d3b9f26713934dd/teasor.gif" width = "800" /> </div> ## <img src="./docs/images/seg_news_icon.png" width="20"/> News <ul class="nobull"> <li>[2023-04-11] :fire: PaddleSeg v2.8 is released! Check more details in <a href="https://github.com/PaddlePaddle/PaddleSeg/releases">Release Notes</a>.</li> <ul> <li>Release <a href="./contrib/SegmentAnything">Segment Anything Model</a> based on PaddlePaddle. Demos are provided to demonstrate the function of automatic full-image segmentation and specified object segmentation with prompt input.</li> <li>Release <a href="./configs/pp_mobileseg">PP-MobileSeg</a>, a lightweight semantic segmentation model for mobile devices. Comparing PP-MobileSeg with other models on the ADE20K dataset, the segmentation accuracy is improved by 1.5%, the inference speed is accelerated by 42.3%, and the number of parameters is decreased by 34.9%. </li> <li>Release <a href="./contrib/QualityInspector">QualityInspector v0.5</a>, a full-process solution for industrial quality inspection. It provides a unified and configurable pipeline for single-task and multi-task models, integrates detection and segmentation model libraries, and supports three unsupervised quality inspection methods. </li> <li>Release <a href="./contrib/PanopticSeg">PanopticSeg v0.5</a>, a universal panoptic segmentation solution. It provides the full-process capabilities of panoptic segmentation, integrates two models, and has flexible secondary development capabilities. </li> </ul> <li>[2022-11-30] PaddleSeg v2.7 released a real-time human matting model <a href="./Matting/">PP-MattingV2</a>, a 3D medical image segmentation solution <a href="./contrib/MedicalSeg/">MedicalSegV2</a>, and a real-time semantic segmentation model <a href="./configs/rtformer/">RTFormer</a>. <li>[2022-07-20] PaddleSeg v2.6 released a real-time human segmentation SOTA solution <a href="./contrib/PP-HumanSeg">PP-HumanSegV2</a>, a stable-version semi-automatic segmentation annotation tool <a href="./EISeg">EISeg v1.0</a>, a pseudo label pre-training method PSSL, and the source code of PP-MattingV1. </li> <li>[2022-04-20] PaddleSeg v2.5 released a real-time semantic segmentation model <a href="./configs/pp_liteseg">PP-LiteSeg</a>, a trimap-free image matting model PP-MattingV1, and an easy-to-use solution for 3D medical image segmentation MedicalSegV1.</li> <li>[2022-01-20] We release PaddleSeg v2.4 with EISeg v0.4, and PP-HumanSegV1 including an open-sourced dataset <a href="./contrib/PP-HumanSeg/paper.md#pp-humanseg14k-a-large-scale-teleconferencing-video-dataset">PP-HumanSeg14K</a>. </li> </ul> ## <img src="https://user-images.githubusercontent.com/48054808/157795569-9fc77c85-732f-4870-9be0-99a7fe2cff27.png" width="20"/> Introduction PaddleSeg is an end-to-end high-efficent development toolkit for image segmentation based on PaddlePaddle, which helps both developers and researchers in the whole process of designing segmentation models, training models, optimizing performance and inference speed, and deploying models. A lot of well-trained models and various real-world applications in both industry and academia help users conveniently build hands-on experiences in image segmentation. <div align="center"> <img src="https://github.com/shiyutang/files/raw/main/teasor_new.gif" width = "800" /> </div> ## <img src="./docs/images/feature.png" width="20"/> Features * **High-Performance Model**: Following the state of the art segmentation methods and using high-performance backbone networks, we provide 45+ models and 150+ high-quality pre-training models, which are better than other open-source implementations. * **High Efficiency**: PaddleSeg provides multi-process asynchronous I/O, multi-card parallel training, evaluation, and other acceleration strategies, combined with the memory optimization function of the PaddlePaddle, which can greatly reduce the training overhead of the segmentation model, all these allowing developers to train image segmentation models more efficiently and at a lower cost. * **Modular Design**: We build PaddleSeg with the modular design philosophy. Therefore, based on actual application scenarios, developers can assemble diversified training configurations with *data augmentation strategies*, *segmentation models*, *backbone networks*, *loss functions*, and other different components to meet different performance and accuracy requirements. * **Complete Flow**: PaddleSeg supports image labeling, model designing, model training, model compression, and model deployment. With the help of PaddleSeg, developers can easily finish all tasks in the entire workflow. <div align="center"> <img src="https://user-images.githubusercontent.com/14087480/176402154-390e5815-1a87-41be-9374-9139c632eb66.png" width = "800" /> </div> ## <img src="./docs/images/chat.png" width="20"/> Community * If you have any questions, suggestions or feature requests, please do not hesitate to create an issue in [GitHub Issues](https://github.com/PaddlePaddle/PaddleSeg/issues). * Please scan the following QR code to join PaddleSeg WeChat group to communicate with us: <div align="center"> <img src="https://user-images.githubusercontent.com/30883834/213601179-0813a896-11e1-4514-b612-d145e068ba86.jpeg" width = "200" /> </div> ## <img src="./docs/images/model.png" width="20"/> Overview <table align="center"> <tbody> <tr align="center" valign="bottom"> <td> <b>Models</b> </td> <td colspan="2"> <b>Components</b> </td> <td> <b>Special Cases</b> </td> </tr> <tr valign="top"> <td> <ul> <details><summary><b>Semantic Segmentation</b></summary> <ul> <li><a href="./configs/pp_liteseg">PP-LiteSeg</a> </li> <li><a href="./configs/pp_mobileseg">PP-MobileSeg</a> </li> <li><a href="./configs/deeplabv3p">DeepLabV3P</a> </li> <li><a href="./configs/ocrnet">OCRNet</a> </li> <li><a href="./configs/mobileseg">MobileSeg</a> </li> <li><a href="./configs/ann">ANN</a></li> <li><a href="./configs/attention_unet">Att U-Net</a></li> <li><a href="./configs/bisenetv1">BiSeNetV1</a></li> <li><a href="./configs/bisenet">BiSeNetV2</a></li> <li><a href="./configs/ccnet">CCNet</a></li> <li><a href="./configs/danet">DANet</a></li> <li><a href="./configs/ddrnet">DDRNet</a></li> <li><a href="./configs/decoupled_segnet">DecoupledSeg</a></li> <li><a href="./configs/deeplabv3">DeepLabV3</a></li> <li><a href="./configs/dmnet">DMNet</a></li> <li><a href="./configs/dnlnet">DNLNet</a></li> <li><a href="./configs/emanet">EMANet</a></li> <li><a href="./configs/encnet">ENCNet</a></li> <li><a href="./configs/enet">ENet</a></li> <li><a href="./configs/espnetv1">ESPNetV1</a></li> <li><a href="./confi