- Fix `LayerDecayOptimizerConstructor` for MAE training ([#1539](https://github.com/open-mmlab/mmsegmentation/pull/1539), [#1540](https://github.com/open-mmlab/mmsegmentation/pull/1540))
### V0.24.0 (4/29/2022)
**Highlights**
- Support MAE: Masked Autoencoders Are Scalable Vision Learners
- Migrate azure blob for beit checkpoints ([1503](https://github.com/open-mmlab/mmsegmentation/pull/1503))
- Fix bug in `tools/analyse_logs.py` caused by wrong plot_iter in some cases ([1428](https://github.com/open-mmlab/mmsegmentation/pull/1428))
**Improvements**
- Merge BEiT and ConvNext's LR decay optimizer constructors ([#1438](https://github.com/open-mmlab/mmsegmentation/pull/1438))
- Register optimizer constructor with mmseg ([#1456](https://github.com/open-mmlab/mmsegmentation/pull/1456))
- Refactor transformer encode layer in ViT and BEiT backbone ([#1481](https://github.com/open-mmlab/mmsegmentation/pull/1481))
- Add `build_pos_embed` and `build_layers` for BEiT ([1517](https://github.com/open-mmlab/mmsegmentation/pull/1517))
- Add `with_cp` to mit and vit ([1431](https://github.com/open-mmlab/mmsegmentation/pull/1431))
- Fix inconsistent dtype of `seg_label` in stdc decode ([1463](https://github.com/open-mmlab/mmsegmentation/pull/1463))
- Delete random seed for training in `dist_train.sh` ([1519](https://github.com/open-mmlab/mmsegmentation/pull/1519))
- Revise high `workers_per_gpus` in config file ([#1506](https://github.com/open-mmlab/mmsegmentation/pull/1506))
- Add GPG keys and del mmcv version in Dockerfile ([1534](https://github.com/open-mmlab/mmsegmentation/pull/1534))
- Update checkpoint for model in deeplabv3plus ([#1487](https://github.com/open-mmlab/mmsegmentation/pull/1487))
- Add `DistSamplerSeedHook` to set epoch number to dataloader when runner is `EpochBasedRunner` ([1449](https://github.com/open-mmlab/mmsegmentation/pull/1449))
- Provide URLs of Swin Transformer pretrained models ([1389](https://github.com/open-mmlab/mmsegmentation/pull/1389))
- Updating Dockerfiles From Docker Directory and `get_started.md` to reach latest stable version of Python, PyTorch and MMCV ([1446](https://github.com/open-mmlab/mmsegmentation/pull/1446))
**Documentation**
- Add more clearly statement of CPU training/inference ([1518](https://github.com/open-mmlab/mmsegmentation/pull/1518))
**Contributors**
-@jiangyitong made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1431
-@kahkeng made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1447
-@Nourollah made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1446
-@androbaza made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1452
-@Yzichen made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1445
-@whu-pzhang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1423
-@panfeng-hover made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1417
-@Johnson-Wang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1496
-@jere357 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1460
-@mfernezir made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1494
-@donglixp made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1503
-@YuanLiuuuuuu made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1307
-@Dawn-bin made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1527
### V0.23.0 (4/1/2022)
**Highlights**
- Support BEiT: BERT Pre-Training of Image Transformers
- Support K-Net: Towards Unified Image Segmentation
- Add `avg_non_ignore` of CELoss to support average loss over non-ignored elements
- Support dataset initialization with file client
**New Features**
- Support BEiT: BERT Pre-Training of Image Transformers ([#1404](https://github.com/open-mmlab/mmsegmentation/pull/1404))
- Support K-Net: Towards Unified Image Segmentation ([#1289](https://github.com/open-mmlab/mmsegmentation/pull/1289))
- Support dataset initialization with file client ([#1402](https://github.com/open-mmlab/mmsegmentation/pull/1402))
- Add class name function for STARE datasets ([#1376](https://github.com/open-mmlab/mmsegmentation/pull/1376))
- Support different seeds on different ranks when distributed training ([#1362](https://github.com/open-mmlab/mmsegmentation/pull/1362))
- Add `nlc2nchw2nlc` and `nchw2nlc2nchw` to simplify tensor with different dimension operation ([#1249](https://github.com/open-mmlab/mmsegmentation/pull/1249))
**Improvements**
- Synchronize random seed for distributed sampler ([#1411](https://github.com/open-mmlab/mmsegmentation/pull/1411))
- Add script and documentation for multi-machine distributed training ([#1383](https://github.com/open-mmlab/mmsegmentation/pull/1383))
**Bug Fixes**
- Add `avg_non_ignore` of CELoss to support average loss over non-ignored elements ([#1409](https://github.com/open-mmlab/mmsegmentation/pull/1409))
- Fix some wrong URLs of models or logs in `./configs` ([#1336](https://github.com/open-mmlab/mmsegmentation/pull/1433))
- Add title and color theme arguments to plot function in `tools/confusion_matrix.py` ([#1401](https://github.com/open-mmlab/mmsegmentation/pull/1401))
- Fix outdated link in Colab demo ([#1392](https://github.com/open-mmlab/mmsegmentation/pull/1392))
- Fix the config name style description in official docs([#1414](https://github.com/open-mmlab/mmsegmentation/pull/1414))
**Contributors**
-@kinglintianxia made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1371
-@CCODING04 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1376
-@mob5566 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1401
-@xiongnemo made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1392
-@Xiangxu-0103 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1405
### V0.22.1 (3/9/2022)
**Bug Fixes**
- Fix the ZeroDivisionError that all pixels in one image is ignored. ([#1336](https://github.com/open-mmlab/mmsegmentation/pull/1336))
**Improvements**
- Provide URLs of STDC, Segmenter and Twins pretrained models ([#1272](https://github.com/open-mmlab/mmsegmentation/pull/1357))
### V0.22 (3/04/2022)
**Highlights**
- Support ConvNeXt: A ConvNet for the 2020s. Please use the latest MMClassification (0.21.0) to try it out.
- Support iSAID aerial Dataset.
- Officially Support inference on Windows OS.
**New Features**
- Support ConvNeXt: A ConvNet for the 2020s. ([#1216](https://github.com/open-mmlab/mmsegmentation/pull/1216))
- Support iSAID aerial Dataset. ([#1115](https://github.com/open-mmlab/mmsegmentation/pull/1115)
- Generating and plotting confusion matrix. ([#1301](https://github.com/open-mmlab/mmsegmentation/pull/1301))
**Improvements**
- Refactor 4 decoder heads (ASPP, FCN, PSP, UPer): Split forward function into `_forward_feature` and `cls_seg`. ([#1299](https://github.com/open-mmlab/mmsegmentation/pull/1299))
- Add `min_size` arg in `Resize` to keep the shape after resize bigger than slide window. ([#1318](https://github.com/open-mmlab/mmsegmentation/pull/1318))
-@JiaYanhao made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1269
-@andife made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1281
-@SBCV made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1279
-@HJoonKwon made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1259
-@Tsingularity made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1290
-@Waterman0524 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1115
-@MeowZheng made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1315
-@linfangjian01 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1318
### V0.21.1 (2/9/2022)
**Bug Fixes**
- Fix typos in docs. ([#1263](https://github.com/open-mmlab/mmsegmentation/pull/1263))
- Fix repeating log by `setup_multi_processes`. ([#1267](https://github.com/open-mmlab/mmsegmentation/pull/1267))
- Upgrade isort in pre-commit hook. ([#1270](https://github.com/open-mmlab/mmsegmentation/pull/1270))
**Improvements**
- Use MMCV load_state_dict func in ViT/Swin. ([#1272](https://github.com/open-mmlab/mmsegmentation/pull/1272))
- Add exception for PointRend for support CPU-only. ([#1271](https://github.com/open-mmlab/mmsegmentation/pull/1270))
### V0.21 (1/29/2022)
**Highlights**
- Officially Support CPUs training and inference, please use the latest MMCV (1.4.4) to try it out.
- Support Segmenter: Transformer for Semantic Segmentation (ICCV'2021).
- Support ISPRS Potsdam and Vaihingen Dataset.
- Add Mosaic transform and `MultiImageMixDataset` class in `dataset_wrappers`.
**New Features**
- Support Segmenter: Transformer for Semantic Segmentation (ICCV'2021) ([#955](https://github.com/open-mmlab/mmsegmentation/pull/955))
- Support ISPRS Potsdam and Vaihingen Dataset ([#1097](https://github.com/open-mmlab/mmsegmentation/pull/1097), [#1171](https://github.com/open-mmlab/mmsegmentation/pull/1171))
- Add segformer‘s benchmark on cityscapes ([#1155](https://github.com/open-mmlab/mmsegmentation/pull/1155))
- Add auto resume ([#1172](https://github.com/open-mmlab/mmsegmentation/pull/1172))
- Add Mosaic transform and `MultiImageMixDataset` class in `dataset_wrappers` ([#1093](https://github.com/open-mmlab/mmsegmentation/pull/1093), [#1105](https://github.com/open-mmlab/mmsegmentation/pull/1105))
- Skip CI on ignoring given paths ([#1078](https://github.com/open-mmlab/mmsegmentation/pull/1078))
- Add abstract and image for every paper ([#1060](https://github.com/open-mmlab/mmsegmentation/pull/1060))
- Create a symbolic link on windows ([#1090](https://github.com/open-mmlab/mmsegmentation/pull/1090))
- Support video demo using trained model ([#1014](https://github.com/open-mmlab/mmsegmentation/pull/1014))
**Bug Fixes**
- Fix incorrectly loading init_cfg or pretrained models of several transformer models ([#999](https://github.com/open-mmlab/mmsegmentation/pull/999), [#1069](https://github.com/open-mmlab/mmsegmentation/pull/1069), [#1102](https://github.com/open-mmlab/mmsegmentation/pull/1102))
- Fix EfficientMultiheadAttention in SegFormer ([#1037](https://github.com/open-mmlab/mmsegmentation/pull/1037))
- Remove `fp16` folder in `configs` ([#1031](https://github.com/open-mmlab/mmsegmentation/pull/1031))
- Fix several typos in .yml file (Dice Metric [#1041](https://github.com/open-mmlab/mmsegmentation/pull/1041), ADE20K dataset [#1120](https://github.com/open-mmlab/mmsegmentation/pull/1120), Training Memory (GB) [#1083](https://github.com/open-mmlab/mmsegmentation/pull/1083))
- Fix test error when using `--show-dir` ([#1091](https://github.com/open-mmlab/mmsegmentation/pull/1091))
- Fix dist training infinite waiting issue ([#1035](https://github.com/open-mmlab/mmsegmentation/pull/1035))
- Change the upper version of mmcv to 1.5.0 ([#1096](https://github.com/open-mmlab/mmsegmentation/pull/1096))
- Fix symlink failure on Windows ([#1038](https://github.com/open-mmlab/mmsegmentation/pull/1038))
- Cancel previous runs that are not completed ([#1118](https://github.com/open-mmlab/mmsegmentation/pull/1118))
- Unified links of readthedocs in docs ([#1119](https://github.com/open-mmlab/mmsegmentation/pull/1119))
**Contributors**
-@Junjue-Wang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1028
-@ddebby made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1066
-@del-zhenwu made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1078
-@KangBK0120 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1106
-@zergzzlun made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1091
-@fingertap made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1035
-@irvingzhang0512 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1014
-@littleSunlxy made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/989
-@lkm2835
-@RockeyCoss
-@MengzhangLI
-@Junjun2016
-@xiexinch
-@xvjiarui
### V0.19 (11/02/2021)
**Highlights**
- Support TIMMBackbone wrapper ([#998](https://github.com/open-mmlab/mmsegmentation/pull/998))
- Support custom hook ([#428](https://github.com/open-mmlab/mmsegmentation/pull/428))
- Add FastFCN benchmark on ADE20K ([#972](https://github.com/open-mmlab/mmsegmentation/pull/972))
**New Features**
- Support TIMMBackbone wrapper ([#998](https://github.com/open-mmlab/mmsegmentation/pull/998))
- Support custom hook ([#428](https://github.com/open-mmlab/mmsegmentation/pull/428))
- Add FastFCN benchmark on ADE20K ([#972](https://github.com/open-mmlab/mmsegmentation/pull/972))
- Add codespell pre-commit hook and fix typos ([#920](https://github.com/open-mmlab/mmsegmentation/pull/920))
**Improvements**
- Make inputs & channels smaller in unittests ([#1004](https://github.com/open-mmlab/mmsegmentation/pull/1004))
- Change `self.loss_decode` back to `dict` in Single Loss situation ([#1002](https://github.com/open-mmlab/mmsegmentation/pull/1002))
**Bug Fixes**
- Fix typo in usage example ([#1003](https://github.com/open-mmlab/mmsegmentation/pull/1003))
- Add contiguous after permutation in ViT ([#992](https://github.com/open-mmlab/mmsegmentation/pull/992))
- Fix the invalid link ([#985](https://github.com/open-mmlab/mmsegmentation/pull/985))
- Fix bug in CI with python 3.9 ([#994](https://github.com/open-mmlab/mmsegmentation/pull/994))
- Fix bug when loading class name form file in custom dataset ([#923](https://github.com/open-mmlab/mmsegmentation/pull/923))
**Contributors**
-@ShoupingShan made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/923
-@RockeyCoss made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/954
-@HarborYuan made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/992
-@lkm2835 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1003
-@gszh made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/428
-@VVsssssk
-@MengzhangLI
-@Junjun2016
### V0.18 (10/07/2021)
**Highlights**
- Support three real-time segmentation models (ICNet [#884](https://github.com/open-mmlab/mmsegmentation/pull/884), BiSeNetV1 [#851](https://github.com/open-mmlab/mmsegmentation/pull/851), and BiSeNetV2 [#804](https://github.com/open-mmlab/mmsegmentation/pull/804))
- Support one efficient segmentation model (FastFCN [#885](https://github.com/open-mmlab/mmsegmentation/pull/885))
- Support one efficient non-local/self-attention based segmentation model (ISANet [#70](https://github.com/open-mmlab/mmsegmentation/pull/70))
- Support COCO-Stuff 10k and 164k datasets ([#625](https://github.com/open-mmlab/mmsegmentation/pull/625))
- Support evaluate concated dataset separately ([#833](https://github.com/open-mmlab/mmsegmentation/pull/833))
- Support loading GT for evaluation from multi-file backend ([#867](https://github.com/open-mmlab/mmsegmentation/pull/867))
**New Features**
- Support three real-time segmentation models (ICNet [#884](https://github.com/open-mmlab/mmsegmentation/pull/884), BiSeNetV1 [#851](https://github.com/open-mmlab/mmsegmentation/pull/851), and BiSeNetV2 [#804](https://github.com/open-mmlab/mmsegmentation/pull/804))
- Support one efficient segmentation model (FastFCN [#885](https://github.com/open-mmlab/mmsegmentation/pull/885))
- Support one efficient non-local/self-attention based segmentation model (ISANet [#70](https://github.com/open-mmlab/mmsegmentation/pull/70))
- Support COCO-Stuff 10k and 164k datasets ([#625](https://github.com/open-mmlab/mmsegmentation/pull/625))
- Support evaluate concated dataset separately ([#833](https://github.com/open-mmlab/mmsegmentation/pull/833))
**Improvements**
- Support loading GT for evaluation from multi-file backend ([#867](https://github.com/open-mmlab/mmsegmentation/pull/867))
- Auto-convert SyncBN to BN when training on DP automatly([#772](https://github.com/open-mmlab/mmsegmentation/pull/772))
- Fix ViT init ([#609](https://github.com/open-mmlab/mmsegmentation/pull/609))
- Fix mmcv version compatible table ([#658](https://github.com/open-mmlab/mmsegmentation/pull/658))
- Fix model links of DMNEt ([#660](https://github.com/open-mmlab/mmsegmentation/pull/660))
**New Features**
- Support loading DeiT weights ([#538](https://github.com/open-mmlab/mmsegmentation/pull/538))
- Support SETR ([#531](https://github.com/open-mmlab/mmsegmentation/pull/531), [#635](https://github.com/open-mmlab/mmsegmentation/pull/635))
- Add config and models for ViT backbone with UperHead ([#520](https://github.com/open-mmlab/mmsegmentation/pull/531), [#635](https://github.com/open-mmlab/mmsegmentation/pull/520))
- Support Swin-Transformer ([#511](https://github.com/open-mmlab/mmsegmentation/pull/511))
- Support dynamic export and visualize to pytorch2onnx ([#463](https://github.com/open-mmlab/mmsegmentation/pull/463))
- Support export to torchscript ([#469](https://github.com/open-mmlab/mmsegmentation/pull/469), [#499](https://github.com/open-mmlab/mmsegmentation/pull/499))
- Support Pascal Context Class-59 dataset ([#459](https://github.com/open-mmlab/mmsegmentation/pull/459))
- Support Visual Transformer backbone ([#465](https://github.com/open-mmlab/mmsegmentation/pull/465))
- Support UpSample Neck ([#512](https://github.com/open-mmlab/mmsegmentation/pull/512))
- Support mFscore metric ([#509](https://github.com/open-mmlab/mmsegmentation/pull/509))
**Improvements**
- Add more CI for PyTorch ([#460](https://github.com/open-mmlab/mmsegmentation/pull/460))
- Add print model graph args for tools/print_config.py ([#451](https://github.com/open-mmlab/mmsegmentation/pull/451))
- Add cfg links in modelzoo README.md ([#468](https://github.com/open-mmlab/mmsegmentation/pull/469))
- Add BaseSegmentor import to segmentors/__init__.py ([#495](https://github.com/open-mmlab/mmsegmentation/pull/495))
