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Release Notes

  • 2020.02.26

    v2.0

    • We newly released version 2.0 which has been fully upgraded to dynamic graphics. It supports more than 20 segmentation models, 4 backbone networks, , 5 datasets and 9 losses:
      • Segmentation models: ANN, BiSeNetV2, DANet, DeeplabV3, DeeplabV3+, FCN, FastSCNN, Gated-scnn, GCNet, HarDNet, OCRNet, PSPNet, UNet, UNet++, U2Net, Attention UNet, Decoupled SegNet, EMANet, DNLNet, ISANet
      • Backbone networks: ResNet, HRNet, MobileNetV3, and Xception
      • Datasets: Cityscapes, ADE20K, Pascal VOC, Pascal Context, COCO Stuff
      • Losses: CrossEntropy Loss, BootstrappedCrossEntropy Loss, Dice Loss, BCE Loss, OhemCrossEntropyLoss, RelaxBoundaryLoss, OhemEdgeAttentionLoss, Lovasz Hinge Loss, Lovasz Softmax Loss
    • We provide more than 50 high quality pre-trained models based on Cityscapes and Pascal Voc datasets.
    • The new version support multi-card GPU parallel evaluation for more efficient metrics calculation. It also support multiple evaluation methods such as multi-scale evaluation/flip evaluation/sliding window evaluation.
    • XPU model training including DeepLabv3, HRNet, UNet, is available now.
    • We open source a semantic segmentation model based on the Hierarchical Multi-Scale Attention structure, and it reached 87% mIoU on the Cityscapes validation set.
    • The dynamic graph mode supports model compression functions such as online quantification and pruning.
    • The dynamic graph mode supports model export for high-performance deployment.

  • 2020.12.18

    v2.0.0-rc

    • Newly release 2.0-rc version, fully upgraded to dynamic graph. It supports 15+ segmentation models, 4 backbone networks, 3 datasets, and 4 types of loss functions:
      • Segmentation models: ANN, BiSeNetV2, DANet, DeeplabV3, DeeplabV3+, FCN, FastSCNN, Gated-scnn, GCNet, OCRNet, PSPNet, UNet, and U2-Net, Attention UNet.
      • Backbone networks: ResNet, HRNet, MobileNetV3, and Xception.
      • Datasets: Cityscapes, ADE20K, and Pascal VOC.
      • Loss: CrossEntropy Loss, BootstrappedCrossEntropy Loss, Dice Loss, BCE Loss.
    • Provide 40+ high quality pre-trained models based on Cityscapes and Pascal Voc datasets.
    • Support multi-card GPU parallel evaluation. This provides the efficient index calculation function. Support multiple evaluation methods such as multi-scale evaluation/flip evaluation/sliding window evaluation.

  • 2020.12.02

    v0.8.0

    • Add multi-scale/flipping/sliding-window inference.
    • Add the fast multi-GPUs evaluation, and high-efficient metric calculation.
    • Add Pascal VOC 2012 dataset.
    • Add high-accuracy pre-trained models on Pascal VOC 2012, see detailed models.
    • Support visualizing pseudo-color images in PNG format while predicting.

  • 2020.10.28

    v0.7.0

    • 全面支持Paddle2.0-rc动态图模式，推出PaddleSeg动态图体验版

    • 发布大量动态图模型，支持11个分割模型，4个骨干网络，3个数据集：

      • 分割模型：ANN, BiSeNetV2, DANet, DeeplabV3, DeeplabV3+, FCN, FastSCNN, GCNet, OCRNet, PSPNet, UNet
      • 骨干网络：ResNet, HRNet, MobileNetV3, Xception
      • 数据集：Cityscapes, ADE20K, Pascal VOC

    • 提供高精度骨干网络预训练模型以及基于Cityscapes数据集的语义分割预训练模型。Cityscapes精度超过82%。

  • 2020.08.31

    v0.6.0

    • 丰富Deeplabv3p网络结构，新增ResNet-vd、MobileNetv3两种backbone，满足高性能与高精度场景，并提供基于Cityscapes和ImageNet的预训练模型4个。
    • 新增高精度分割模型OCRNet，支持以HRNet作为backbone，提供基于Cityscapes的预训练模型，mIoU超过80%。
    • 新增proposal free的实例分割模型Spatial Embedding，性能与精度均超越MaskRCNN。提供了基于kitti的预训练模型。

  • 2020.05.12

    v0.5.0

    • 全面升级HumanSeg人像分割模型，新增超轻量级人像分割模型HumanSeg-lite支持移动端实时人像分割处理，并提供基于光流的视频分割后处理提升分割流畅性。
    • 新增气象遥感分割方案，支持积雪识别、云检测等气象遥感场景。
    • 新增Lovasz Loss，解决数据类别不均衡问题。
    • 使用VisualDL 2.0作为训练可视化工具

  • 2020.02.25

    v0.4.0

    • 新增适用于实时场景且不需要预训练模型的分割网络Fast-SCNN，提供基于Cityscapes的预训练模型1个
    • 新增LaneNet车道线检测网络，提供预训练模型一个
    • 新增基于PaddleSlim的分割库压缩策略(量化, 蒸馏, 剪枝, 搜索)

  • 2019.12.15

    v0.3.0

    • 新增HRNet分割网络，提供基于cityscapes和ImageNet的预训练模型8个
    • 支持使用伪彩色标签进行训练/评估/预测，提升训练体验，并提供将灰度标注图转为伪彩色标注图的脚本
    • 新增学习率warmup功能，支持与不同的学习率Decay策略配合使用
    • 新增图像归一化操作的GPU化实现，进一步提升预测速度。
    • 新增Python部署方案，更低成本完成工业级部署。
    • 新增Paddle-Lite移动端部署方案，支持人像分割模型的移动端部署。
    • 新增不同分割模型的预测性能数据Benchmark, 便于开发者提供模型选型性能参考。

  • 2019.11.04

    v0.2.0

  • 2019.09.10

    v0.1.0

    • PaddleSeg分割库初始版本发布，包含DeepLabv3+, U-Net, ICNet三类分割模型, 其中DeepLabv3+支持Xception, MobileNet v2两种可调节的骨干网络。
    • CVPR19 LIP人体部件分割比赛冠军预测模型发布ACE2P。
    • 预置基于DeepLabv3+网络的人像分割和车道线分割预测模型发布。