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PaddleSeg v2.7.0
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@juncaipeng juncaipeng released this 30 Nov 06:56
· 23 commits to release/2.7 since this release
v2.7.0
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Learn about vigilant mode.

New Features

Semantic Segmentation

  • Release RTFormer, a real-time semantic segmentation model accepted by NeurIPS 2022. RTFormer combines the advantages of CNN and Transformer modules, and it achieves SOTA trade-off between performance and efficiency on several datasets.
  • Release UHRNet, a semantic segmentation model. The segmentation accuracy of UHRNet is higher than that of HRNet on Cityscapes.
  • Add 2 semantic segmentation models, i.e., TopFormer and MscaleOCRNet-PSA.
  • Enhance model training module, i.e., training for single channel images, setting different learning rate for backbone and head.
  • Add the tutorials of config preparation and training tricks.

Image Matting

  • Release PP-MattingV2, a real-time human matting model with SOTA performance. Compared to previous models, the mean error is reduced by 17.91%, the inference speed is improved by 44.6% on GPU.
  • Refine the tutorials and build the benchmark of Matting models.

3D Medical Segmentation

  • Release MedicalSegV2, a superior 3D medical image segmentation solution.
    • Release an intelligent annotation toolkit called EISeg-Med3D.
    • Release an optimized implementation of nnUNet named nnUNet-D, which has model deployment module.
    • Add 3 segmentation models, i.e., SwinUnet, TransUnet and nnFormer.
    • Refine the tutorials, add detailed information of model zoo and model introduction.

新特性

语义分割

  • 发布轻量级语义分割模型RTFormer，结合CNN和Transformer模块的优势，在公开数据集上实现性能SOTA，并发表于NeurIPS 2022。
  • 发布高精度语义分割模型UHRNet，Cityscapes数据集上精度超越HRNet。
  • 新增2个语义分割模型，TopFormer和MscaleOCRNet-PSA。
  • 增强训练功能，支持单通道图像训练，支持Backbone和Head设置不同学习率。
  • 优化安装步骤和文档，新增准备配置文件、高阶训练技巧的教程。

深度抠图

  • 发布自研的轻量级抠图模型PP-MattingV2，推理速度提升44.6%，平均误差减小17.91%，超越此前SOTA模型，支持零成本开箱即用。
  • 升级文档和教程，构建模型库Benchmark。

3D医疗分割

  • 发布3D医疗影像分割方案MedicalSegV2。
    • 发布3D医疗影像交互式标注工具EISeg-Med3D，具有算法创新、高效准确、用户友好等优势。
    • 新增前沿高精度分割方案nnUNet-D，涵盖数据分析、超参优化、模型构建、模型训练、模型融合等模块，而且新增模型部署的能力。
    • 新增3个医疗影像分割模型，SwinUnet、TransUnet和nnFormer，精度超过原论文最高达到3.6%。
    • 升级医疗分割的文档和教程，丰富ModelZoo和模型原理说明。

Bug Fix

  • Fix data transforms error in PanopticDeeplab. #2381
  • Fix evaluation error for cityscapes dataset in PanopticDeeplab. #2564
  • Replace _C_ops with _legacy_C_ops for basic api. #2494
  • Check and synchronize the num_classes in config. #2477
  • Replace with_softmax and with_argmax with output_op in export.py. #2547
  • Correct the use of dist.all_reduce in distributed training. #2606
  • Fix the error of releasing dataloader. #2650
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