github.com/PaddlePaddle/PaddleSeg @v2.10.0 sqlite

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A High-Efficient Development Toolkit for Image Segmentation Based on PaddlePaddle.

News

• [2023-10-29] :fire: PaddleSeg v2.9 is released! Check more details in Release Notes.
• Support Multi-label segmentation, it procides multi-label segmentation support on a serie of semantic segmetation models.
• Release Mobile SAM, faster version of Segment Anything Model.
• Support Quant Aware Distillation Training Compression for PP-LiteSeg, PP-MobileSeg, OCRNet, and SegFormer-B0 to improve model inference speed.
• [2022-04-11] PaddleSeg v2.8 released Segment Anything Model, an original light-weight semantic segmentation model on mobile devices PP-MobileSeg, QualityInspector v0.5, a full-process solution for industrial quality inspection, and PanopticSeg v0.5, a universal panoptic segmentation solution.
• [2022-11-30] PaddleSeg v2.7 released a real-time human matting model PP-MattingV2, a 3D medical image segmentation solution MedicalSegV2, and a real-time semantic segmentation model RTFormer.
• [2022-07-20] PaddleSeg v2.6 released a real-time human segmentation SOTA solution PP-HumanSegV2, a stable-version semi-automatic segmentation annotation tool EISeg v1.0, a pseudo label pre-training method PSSL, and the source code of PP-MattingV1.
• [2022-04-20] PaddleSeg v2.5 released a real-time semantic segmentation model PP-LiteSeg, a trimap-free image matting model PP-MattingV1, and an easy-to-use solution for 3D medical image segmentation MedicalSegV1.
• [2022-01-20] We release PaddleSeg v2.4 with EISeg v0.4, and PP-HumanSegV1 including an open-sourced dataset PP-HumanSeg14K.

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.

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.

Community

• If you have any questions, suggestions or feature requests, please do not hesitate to create an issue in GitHub Issues.
• Please scan the following QR code to join PaddleSeg WeChat group to communicate with us:

Overview