Mmsegmentation

**Highlights**

- Support memory efficient test, add more UNet models.

**Bug Fixes**

- Fixed TTA resize scale ([334](https://github.com/open-mmlab/mmsegmentation/pull/334))
- Fixed CI for pip 20.3 ([307](https://github.com/open-mmlab/mmsegmentation/pull/307))
- Fixed ADE20k test ([359](https://github.com/open-mmlab/mmsegmentation/pull/359))

**New Features**

- Support memory efficient test ([330](https://github.com/open-mmlab/mmsegmentation/pull/330))
- Add more UNet benchmarks ([324](https://github.com/open-mmlab/mmsegmentation/pull/324))
- Support Lovasz Loss ([351](https://github.com/open-mmlab/mmsegmentation/pull/351))

**Improvements**

- Move train_cfg/test_cfg inside model ([341](https://github.com/open-mmlab/mmsegmentation/pull/341))

**Highlights**

- Support MobileNetV3, DMNet, APCNet. Add models of ResNet18V1b, ResNet18V1c, ResNet50V1b, ResNet101V1b.

**Bug Fixes**

- Fixed CPU TTA ([276](https://github.com/open-mmlab/mmsegmentation/pull/276))
- Fixed CI for pip 20.3 ([307](https://github.com/open-mmlab/mmsegmentation/pull/307))

**New Features**

- Add ResNet18V1b, ResNet18V1c, ResNet50V1b models ([316](https://github.com/open-mmlab/mmsegmentation/pull/316))
- Support MobileNetV3 ([268](https://github.com/open-mmlab/mmsegmentation/pull/268))
- Add 4 retinal vessel segmentation benchmark ([315](https://github.com/open-mmlab/mmsegmentation/pull/315))
- Support DMNet ([313](https://github.com/open-mmlab/mmsegmentation/pull/313))
- Support APCNet ([299](https://github.com/open-mmlab/mmsegmentation/pull/299))

**Improvements**

- Refactor Documentation page ([311](https://github.com/open-mmlab/mmsegmentation/pull/311))
- Support resize data augmentation according to original image size ([291](https://github.com/open-mmlab/mmsegmentation/pull/291))

**Highlights**

- Add 5 transform augmentation, add model statistics.

**New Features**

- Support RandomRotate transform ([215](https://github.com/open-mmlab/mmsegmentation/pull/215), [#260](https://github.com/open-mmlab/mmsegmentation/pull/260))
- Support RGB2Gray transform ([227](https://github.com/open-mmlab/mmsegmentation/pull/227))
- Support Rerange transform ([228](https://github.com/open-mmlab/mmsegmentation/pull/228))
- Support ignore_index for BCE loss ([210](https://github.com/open-mmlab/mmsegmentation/pull/210))
- Add modelzoo statistics ([263](https://github.com/open-mmlab/mmsegmentation/pull/263))
- Support Dice evaluation metric ([225](https://github.com/open-mmlab/mmsegmentation/pull/225))
- Support Adjust Gamma transform ([232](https://github.com/open-mmlab/mmsegmentation/pull/232))
- Support CLAHE transform ([229](https://github.com/open-mmlab/mmsegmentation/pull/229))

**Bug Fixes**

- Fixed detail API link ([267](https://github.com/open-mmlab/mmsegmentation/pull/267))

**Highlights**

- Support 4 medical dataset, UNet and CGNet.

**New Features**

- Support customize runner ([118](https://github.com/open-mmlab/mmsegmentation/pull/118))
- Support UNet ([161](https://github.com/open-mmlab/mmsegmentation/pull/162))
- Support CHASE_DB1, DRIVE, STARE, HRD ([203](https://github.com/open-mmlab/mmsegmentation/pull/203))
- Support CGNet ([223](https://github.com/open-mmlab/mmsegmentation/pull/223))

**Highlights**

- Support Pascal Context dataset and customizing class dataset.

**Bug Fixes**

- Fixed CPU inference ([153](https://github.com/open-mmlab/mmsegmentation/pull/153))

**New Features**

- Add DeepLab OS16 models ([154](https://github.com/open-mmlab/mmsegmentation/pull/154))
- Support Pascal Context dataset ([133](https://github.com/open-mmlab/mmsegmentation/pull/133))
- Support customizing dataset classes ([71](https://github.com/open-mmlab/mmsegmentation/pull/71))
- Support customizing dataset palette ([157](https://github.com/open-mmlab/mmsegmentation/pull/157))

**Improvements**

- Support 4D tensor output in ONNX ([150](https://github.com/open-mmlab/mmsegmentation/pull/150))
- Remove redundancies in ONNX export ([160](https://github.com/open-mmlab/mmsegmentation/pull/160))
- Migrate to MMCV DepthwiseSeparableConv ([158](https://github.com/open-mmlab/mmsegmentation/pull/158))
- Migrate to MMCV collect_env ([137](https://github.com/open-mmlab/mmsegmentation/pull/137))
- Use img_prefix and seg_prefix for loading ([153](https://github.com/open-mmlab/mmsegmentation/pull/153))

**Highlights**

- Support new methods i.e. MobileNetV2, EMANet, DNL, PointRend, Semantic FPN, Fast-SCNN, ResNeSt.

**Bug Fixes**

- Fixed sliding inference ONNX export ([90](https://github.com/open-mmlab/mmsegmentation/pull/90))

**New Features**

- Support MobileNet v2 ([86](https://github.com/open-mmlab/mmsegmentation/pull/86))
- Support EMANet ([34](https://github.com/open-mmlab/mmsegmentation/pull/34))
- Support DNL ([37](https://github.com/open-mmlab/mmsegmentation/pull/37))
- Support PointRend ([109](https://github.com/open-mmlab/mmsegmentation/pull/109))
- Support Semantic FPN ([94](https://github.com/open-mmlab/mmsegmentation/pull/94))
- Support Fast-SCNN ([58](https://github.com/open-mmlab/mmsegmentation/pull/58))
- Support ResNeSt backbone ([47](https://github.com/open-mmlab/mmsegmentation/pull/47))
- Support ONNX export (experimental) ([12](https://github.com/open-mmlab/mmsegmentation/pull/12))

**Improvements**

- Support Upsample in ONNX ([100](https://github.com/open-mmlab/mmsegmentation/pull/100))
- Support Windows install (experimental) ([75](https://github.com/open-mmlab/mmsegmentation/pull/75))
- Add more OCRNet results ([20](https://github.com/open-mmlab/mmsegmentation/pull/20))
- Add PyTorch 1.6 CI ([64](https://github.com/open-mmlab/mmsegmentation/pull/64))
- Get version and githash automatically ([55](https://github.com/open-mmlab/mmsegmentation/pull/55))