PaddleSeg v2.6.0
·
72 commits
to release/2.6
since this release
New Features
Semantic Segmentation
- Release PP-HumanSeg v2, an off-the-shelf human segmentation model. It achieves 64.26 FPS on the mobile device, which is 45.5% faster than before.
- Release PSSL, a novel pre-training method, including a large dataset that consists of 1.2M+ pseudo semantic segmentation labels (PSSL) corresponding to the whole ImageNet training set. It boosts the performances of various models on all downstream tasks.
- Release the industrial model series: high-accuracy models, light-weight models, and super light-weight models, to help developers pick up the most suitable one.
- Add 2 segmentation models: MobileNetV3_LRASPP，UperNet.
- Add 1 initialization method: Xavier Uniform.
- Upgrade data reading pipeline that allows using dict to pass the data.
- Support PaddleSMRT which is a model selection tool that help developers to choose the best model according to the actual requirements.
- Upgrade the homepage, and provide more easy-to-use quick-start tutorial.
Intelligent Labelling
- Release EISeg v1.0, the stable-version semi-automatic tool for image, video and 3D slice data annotation. It achieves "Once for All" (training once, and labelling all) performance.
- Add interactive video object segmentation for general scenes, this work is based on EISeg interactive segmentation model and MiVOS.
- Add 3D segmentation capabilities for abdominal multi-organ and CT vertebral data, and provides 3D visualization tools.
Image Matting
- Release PP-Matting source code and the pre-trained models.
- Add the pymatting package that provides five traditional matting methods including ClosedFormMatting、KNNMatting, FastMatting, LearningBaseMatting, and RandomWalksMatting.
- Add GCA model, update the ppmatting architecture, and support user-specified metrics evaluations.
3D Medical Segmentation
- Add UNETR，we achieve Dice=71.8% in MSD-brain, which is 0.7% higher than the original implementation.
- Add slicing window prediction to support large-scale input, which improves the inference accuracy.
新特性
语义分割
- 发布实时人像分割模型PP-HumanSeg v2，移动端推理速度提升45.5%、达到64.26 FPS，分割精度更高、通用型更强、零成本开箱即用。
- 发布120多万张ImageNet分割伪标签数据集，以及预训练方法PSSL，全面提升分割模型在下游任务上的性能。
- 发布产业级语义分割模型，包括高精度、轻量级和超轻量级系列。
- 新增2个语义分割模型，MobileNetV3_LRASPP，UperNet。
- 新增1个初始化方法：Xavier Uniform。
- 升级数据流程，通过字典形式进行数据的传递，提升数据流的可读性、灵活性与扩展性。
- 接入飞桨产业模型选型工具PaddleSMRT，可以根据产业落地的诸多诉求，分析数据特点，推荐合适的模型和方案。
- 全新升级文档主页，全流程使用教程更加详实。
智能标注
- 发布高性能智能标注工具EISeg v1.0正式版，实现一次训练万物可标，加速提升图像、视频、3D医疗影像等领域的分割标注效率。
- 新增用于通用场景视频交互式分割能力，以EISeg交互式分割模型及MiVOS算法为基础，全面提升视频标注体验。
- 新增用于腹腔多器官及CT椎骨数据3D分割能力，并提供3D可视化工具，给予医疗领域3D标注新的思路。
深度抠图
- 开源PP-Matting代码和预训练模型
- 新增pymatting支持，引入ClosedFormMatting、KNNMatting、FastMatting、LearningBaseMatting和RandomWalksMatting传统机器学习算法。
- 新增GCA模型，更新目录结构，支持指定指标进行评估。
3D医疗分割
- 新增前沿模型UNETR，在MSD-brain 上Dice为71.8%，高于原论文0.7%。
- 新增滑窗预测功能，支持大图推理提升精度。