新特性

全新发布2.0-rc版本，全面升级至动态图，支持15+分割模型，4个骨干网络，3个数据集，4种Loss：
- 分割模型：ANN, BiSeNetV2, DANet, DeeplabV3, DeeplabV3+, FCN, FastSCNN, Gated-scnn, GCNet, HarDNet, OCRNet, PSPNet, UNet, UNet++, U^2Net, Attention UNet
- 骨干网络：ResNet, HRNet, MobileNetV3, Xception
- 数据集：Cityscapes, ADE20K, Pascal VOC
- Loss：CrossEntropy Loss、BootstrappedCrossEntropy Loss、Dice Loss、BCE Loss
提供基于Cityscapes和Pascal Voc数据集的高质量预训练模型 40+。
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。

New Features

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 U^2Net
- 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.