新特性

全新发布2.0版本，全面升级至动态图，支持20+分割模型，4个骨干网络，5个数据集，9种Loss：
- 分割模型：ANN、BiSeNetV2、DANet、DeeplabV3、DeeplabV3+、FCN、FastSCNN、Gated-scnn、GCNet、HarDNet、OCRNet、PSPNet、UNet、UNet++、U²Net、Attention UNet、Decoupled SegNet、EMANet、DNLNet、ISANet
- 骨干网络：ResNet, HRNet, MobileNetV3, Xception
- 数据集：Cityscapes, ADE20K, Pascal VOC, Pascal Context, COCO Stuff
- Loss：CrossEntropy Loss、BootstrappedCrossEntropy Loss、Dice Loss、BCE Loss、OhemCrossEntropyLoss、RelaxBoundaryLoss、OhemEdgeAttentionLoss、Lovasz Hinge Loss、Lovasz Softmax Loss
提供基于Cityscapes和Pascal Voc数据集的高质量预训练模型 50+
支持多卡GPU并行评估，提供了高效的指标计算功能。支持多尺度评估/翻转评估/滑动窗口评估等多种评估方式。
支持XPU模型训练，包括DeepLabv3、HRNet、UNet。
开源了基于Hierarchical Multi-Scale Attention结构的语义分割模型，在Cityscapes验证集上达到87% mIoU。
动态图模式支持模型在线量化、剪枝等模型压缩功能。
动态图下支持模型动转静，实现高性能部署。

New Features

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.