Changelog

V0.30.0 (01/09/2023)

New Features

• Support Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets (#2194)

Bug Fixes

• Fix incorrect test_cfg setting in UNet base configs (#2347)
• Fix KNet IterativeDecodeHead bug in master branch (#2333)
• Fix deadlock issue related with MMSegWandbHook (#2398)

Enhancement

• Update CI and pre-commit checking (#2309,#2331)
• Add Projects/ folder, and the first example project in 0.x (#2457)
• Fix the deprecation of np.float and CI configuration problems (#2451)

Documentation

• Add high quality synthetic face occlusion dataset link to readme (#2453)
• Fix the docstring error in the PascalContextDataset59 class (#2450)

Contributors

• @smttsp made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2347
• @MilkClouds made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2398
• @Spritea made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2450

V0.29.1 (11/3/2022)

New Features

• Add model ensemble tools (#2218)

Bug Fixes

• Use SyncBN in MobileNetV2 (#2207)

Documentation

• Update FAQ doc about binary segmentation and ReduceZeroLabel (#2206)
• Fix typos (#2249)
• Fix model results (#2190, #2114)

Contributors

• @isLinXu made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2219
• @zhijiejia made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2218
• @lee-jinhee made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2249

V0.29.0 (10/10/2022)

New Features

• Support PoolFormer (CVPR'2022) (#1537)

Enhancement

• Improve structure and readability for FCNHead (#2142)
• Support IterableDataset in distributed training (#2151)
• Upgrade .dev scripts (#2020)
• Upgrade pre-commit hooks (#2155)

Bug Fixes

• Fix mmseg.api.inference inference_segmentor (#1849)
• fix bug about label_map in evaluation part (#2075)
• Add missing dependencies to torchserve docker file (#2133)
• Fix ddp unittest (#2060)

Contributors

• @jinwonkim93 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1849
• @rlatjcj made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2075
• @ShirleyWangCVR made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2151
• @mangelroman made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2133

V0.28.0 (9/8/2022)

New Features

• Support Tversky Loss (#1896)

Bug Fixes

• Fix binary segmentation (#2016)
• Fix config files (#1901, #1893, #1871)
• Revise documentation (#1844, #1980, #2025, #1982)
• Fix confusion matrix calculation (#1992)
• Fix decode head forward_train error (#1997)

Contributors

• @suchot made their first contribution in https://github.com/open-mmlab/mmsegmention/pull/1844
• @TimoK93 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1992

V0.27.0 (7/28/2022)

Enhancement

• Add Swin-L Transformer models (#1471)
• Update ERFNet results (#1744)

Bug Fixes

• Revise documentation (#1761, #1755, #1802)
• Fix colab tutorial (#1779)
• Fix segformer checkpoint url (#1785)

Contributors

• @DataSttructure made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1802
• @AkideLiu made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1785
• @mawanda-jun made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1761
• @Yan-Daojiang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1755

V0.26.0 (7/1/2022)

Highlights

• Update New SegFormer models on ADE20K (1705)
• Dedicated MMSegWandbHook for MMSegmentation (1603)

New Features

• Update New SegFormer models on ADE20K (1705)
• Dedicated MMSegWandbHook for MMSegmentation (1603)
• Add UPerNet r18 results (1669)

Enhancement

• Keep dimension of cls_token_weight for easier ONNX deployment (1642)
• Support infererence with padding (1607)

Bug Fixes

• Fix typos (#1640, #1667, #1656, #1699, #1702, #1695, #1707, #1708, #1721)

Documentation

• Fix mdformat version to support python3.6 and remove ruby installation (1672)

Contributors

• @RunningLeon made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1642
• @zhouzaida made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1655
• @tkhe made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1667
• @rotorliu made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1656
• @EvelynWang-0423 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1679
• @ZhaoYi1222 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1616
• @Sanster made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1704
• @ayulockin made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1603

V0.25.0 (6/2/2022)

Highlights

• Support PyTorch backend on MLU (1515)

Bug Fixes

• Fix the error of BCE loss when batch size is 1 (1629)
• Fix bug of resize function when align_corners is True (1592)
• Fix Dockerfile to run demo script in docker container (1568)
• Correct inference_demo.ipynb path (1576)
• Fix the build_segmentor in colab demo (1551)
• Fix md2yml script (1633, 1555)
• Fix main line link in MAE README.md (1556)
• Fix fastfcn crop_size in README.md by (1597)
• Pip upgrade when testing windows platform (1610)

Improvements

• Delete DS_Store file (1549)
• Revise owners.yml (1621, 1534)

Documentation

• Rewrite the installation guidance (1630)
• Format readme (1635)
• Replace markdownlint with mdformat to avoid ruby installation (1591)
• Add explanation and usage instructions for data configuration (1548)
• Configure Myst-parser to parse anchor tag (1589)
• Update QR code and link for QQ group (1598, 1574)

Contributors

• @atinfinity made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1568
• @DoubleChuang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1576
• @alpha-baymax made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1515
• @274869388 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1629

V0.24.1 (5/1/2022)

Bug Fixes

• Fix LayerDecayOptimizerConstructor for MAE training (#1539, #1540)

V0.24.0 (4/29/2022)

Highlights

• Support MAE: Masked Autoencoders Are Scalable Vision Learners
• Support Resnet strikes back

New Features

• Support MAE: Masked Autoencoders Are Scalable Vision Learners (1307, 1523)
• Support Resnet strikes back (1390)
• Support extra dataloader settings in configs (1435)

Bug Fixes

• Fix input previous results for the last cascade_decode_head (#1450)
• Fix validation loss logging (#1494)
• Fix the bug in binary_cross_entropy (1527)
• Support single channel prediction for Binary Cross Entropy Loss (#1454)
• Fix potential bugs in accuracy.py (1496)
• Avoid converting label ids twice by label map during evaluation (1417)
• Fix bug about label_map (1445)
• Fix image save path bug in Windows (1423)
• Fix MMSegmentation Colab demo (1501, 1452)
• Migrate azure blob for beit checkpoints (1503)
• Fix bug in tools/analyse_logs.py caused by wrong plot_iter in some cases (1428)

Improvements

• Merge BEiT and ConvNext's LR decay optimizer constructors (#1438)
• Register optimizer constructor with mmseg (#1456)
• Refactor transformer encode layer in ViT and BEiT backbone (#1481)
• Add build_pos_embed and build_layers for BEiT (1517)
• Add with_cp to mit and vit (1431)
• Fix inconsistent dtype of seg_label in stdc decode (1463)
• Delete random seed for training in dist_train.sh (1519)
• Revise high workers_per_gpus in config file (#1506)
• Add GPG keys and del mmcv version in Dockerfile (1534)
• Update checkpoint for model in deeplabv3plus (#1487)
• Add DistSamplerSeedHook to set epoch number to dataloader when runner is EpochBasedRunner (1449)
• Provide URLs of Swin Transformer pretrained models (1389)
• Updating Dockerfiles From Docker Directory and get_started.md to reach latest stable version of Python, PyTorch and MMCV (1446)

Documentation

• Add more clearly statement of CPU training/inference (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)
• Support K-Net: Towards Unified Image Segmentation (#1289)
• Support dataset initialization with file client (#1402)
• Add class name function for STARE datasets (#1376)
• Support different seeds on different ranks when distributed training (#1362)
• Add nlc2nchw2nlc and nchw2nlc2nchw to simplify tensor with different dimension operation (#1249)

Improvements

• Synchronize random seed for distributed sampler (#1411)
• Add script and documentation for multi-machine distributed training (#1383)

Bug Fixes

• Add avg_non_ignore of CELoss to support average loss over non-ignored elements (#1409)
• Fix some wrong URLs of models or logs in ./configs (#1336)
• Add title and color theme arguments to plot function in tools/confusion_matrix.py (#1401)
• Fix outdated link in Colab demo (#1392)
• Fix typos (#1424, #1405, #1371, #1366, #1363)

Documentation

• Add FAQ document (#1420)
• Fix the config name style description in official docs(#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)

Improvements

• Provide URLs of STDC, Segmenter and Twins pretrained models (#1272)

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)
• Support iSAID aerial Dataset. (#1115
• Generating and plotting confusion matrix. (#1301)

Improvements

• Refactor 4 decoder heads (ASPP, FCN, PSP, UPer): Split forward function into _forward_feature and cls_seg. (#1299)
• Add min_size arg in Resize to keep the shape after resize bigger than slide window. (#1318)
• Revise pre-commit-hooks. (#1315)
• Add win-ci. (#1296)

Bug Fixes

• Fix mlp_ratio type in Swin Transformer. (#1274)
• Fix path errors in ./demo . (#1269)
• Fix bug in conversion of potsdam. (#1279)
• Make accuracy take into account ignore_index. (#1259)
• Add Pytorch HardSwish assertion in unit test. (#1294)
• Fix wrong palette value in vaihingen. (#1292)
• Fix the bug that SETR cannot load pretrain. (#1293)
• Update correct In Collection in metafile of each configs. (#1239)
• Upload completed STDC models. (#1332)
• Fix DNLHead exports onnx inference difference type Cast error. (#1161)

Contributors

• @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)
• Fix repeating log by setup_multi_processes. (#1267)
• Upgrade isort in pre-commit hook. (#1270)

Improvements

• Use MMCV load_state_dict func in ViT/Swin. (#1272)
• Add exception for PointRend for support CPU-only. (#1271)

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)
• Support ISPRS Potsdam and Vaihingen Dataset (#1097, #1171)
• Add segformer‘s benchmark on cityscapes (#1155)
• Add auto resume (#1172)
• Add Mosaic transform and MultiImageMixDataset class in dataset_wrappers (#1093, #1105)
• Add log collector (#1175)

Improvements

• New-style CPU training and inference (#1251)
• Add UNet benchmark with multiple losses supervision (#1143)

Bug Fixes

• Fix the model statistics in doc for readthedoc (#1153)
• Set random seed for palette if not given (#1152)
• Add COCOStuffDataset in class_names.py (#1222)
• Fix bug in non-distributed multi-gpu training/testing (#1247)
• Delete unnecessary lines of STDCHead (#1231)

Contributors

• @jbwang1997 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1152
• @BeaverCC made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1206
• @Echo-minn made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1214
• @rstrudel made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/955

V0.20.2 (12/15/2021)

Bug Fixes

• Revise --option to --options to avoid BC-breaking. (#1140)

V0.20.1 (12/14/2021)

Improvements

• Change options to cfg-options (#1129)

Bug Fixes

• Fix <!-- [ABSTRACT] --> in metafile. (#1127)
• Fix correct num_classes of HRNet in LoveDA dataset (#1136)

V0.20 (12/10/2021)

Highlights

• Support Twins (#989)
• Support a real-time segmentation model STDC (#995)
• Support a widely-used segmentation model in lane detection ERFNet (#960)
• Support A Remote Sensing Land-Cover Dataset LoveDA (#1028)
• Support focal loss (#1024)

New Features

• Support Twins (#989)
• Support a real-time segmentation model STDC (#995)
• Support a widely-used segmentation model in lane detection ERFNet (#960)
• Add SETR cityscapes benchmark (#1087)
• Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019)
• Support focal loss (#1024)
• Add Cutout transform (#1022)

Improvements

• Set a random seed when the user does not set a seed (#1039)
• Add CircleCI setup (#1086)
• Skip CI on ignoring given paths (#1078)
• Add abstract and image for every paper (#1060)
• Create a symbolic link on windows (#1090)
• Support video demo using trained model (#1014)

Bug Fixes

• Fix incorrectly loading init_cfg or pretrained models of several transformer models (#999, #1069, #1102)
• Fix EfficientMultiheadAttention in SegFormer (#1037)
• Remove fp16 folder in configs (#1031)
• Fix several typos in .yml file (Dice Metric #1041, ADE20K dataset #1120, Training Memory (GB) #1083)
• Fix test error when using --show-dir (#1091)
• Fix dist training infinite waiting issue (#1035)
• Change the upper version of mmcv to 1.5.0 (#1096)
• Fix symlink failure on Windows (#1038)
• Cancel previous runs that are not completed (#1118)
• Unified links of readthedocs in docs (#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)
• Support custom hook (#428)
• Add codespell pre-commit hook (#920)
• Add FastFCN benchmark on ADE20K (#972)

New Features

• Support TIMMBackbone wrapper (#998)
• Support custom hook (#428)
• Add FastFCN benchmark on ADE20K (#972)
• Add codespell pre-commit hook and fix typos (#920)

Improvements

• Make inputs & channels smaller in unittests (#1004)
• Change self.loss_decode back to dict in Single Loss situation (#1002)

Bug Fixes

• Fix typo in usage example (#1003)
• Add contiguous after permutation in ViT (#992)
• Fix the invalid link (#985)
• Fix bug in CI with python 3.9 (#994)
• Fix bug when loading class name form file in custom dataset (#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, BiSeNetV1 #851, and BiSeNetV2 #804)
• Support one efficient segmentation model (FastFCN #885)
• Support one efficient non-local/self-attention based segmentation model (ISANet #70)
• Support COCO-Stuff 10k and 164k datasets (#625)
• Support evaluate concated dataset separately (#833)
• Support loading GT for evaluation from multi-file backend (#867)

New Features

• Support three real-time segmentation models (ICNet #884, BiSeNetV1 #851, and BiSeNetV2 #804)
• Support one efficient segmentation model (FastFCN #885)
• Support one efficient non-local/self-attention based segmentation model (ISANet #70)
• Support COCO-Stuff 10k and 164k datasets (#625)
• Support evaluate concated dataset separately (#833)

Improvements

• Support loading GT for evaluation from multi-file backend (#867)
• Auto-convert SyncBN to BN when training on DP automatly(#772)
• Refactor Swin-Transformer (#800)

Bug Fixes

• Update mmcv installation in dockerfile (#860)
• Fix number of iteration bug when resuming checkpoint in distributed train (#866)
• Fix parsing parse in val_step (#906)

V0.17 (09/01/2021)

Highlights

• Support SegFormer
• Support DPT
• Support Dark Zurich and Nighttime Driving datasets
• Support progressive evaluation

New Features

• Support SegFormer (#599)
• Support DPT (#605)
• Support Dark Zurich and Nighttime Driving datasets (#815)
• Support progressive evaluation (#709)

Improvements

• Add multiscale_output interface and unittests for HRNet (#830)
• Support inherit cityscapes dataset (#750)
• Fix some typos in README.md (#824)
• Delete convert function and add instruction to ViT/Swin README.md (#791)
• Add vit/swin/mit convert weight scripts (#783)
• Add copyright files (#796)

Bug Fixes

• Fix invalid checkpoint link in inference_demo.ipynb (#814)
• Ensure that items in dataset have the same order across multi machine (#780)
• Fix the log error (#766)

V0.16 (08/04/2021)

Highlights

• Support PyTorch 1.9
• Support SegFormer backbone MiT
• Support md2yml pre-commit hook
• Support frozen stage for HRNet

New Features

• Support SegFormer backbone MiT (#594)
• Support md2yml pre-commit hook (#732)
• Support mim (#717)
• Add mmseg2torchserve tool (#552)

Improvements

• Support hrnet frozen stage (#743)
• Add template of reimplementation questions (#741)
• Output pdf and epub formats for readthedocs (#742)
• Refine the docstring of ResNet (#723)
• Replace interpolate with resize (#731)
• Update resource limit (#700)
• Update config.md (#678)

Bug Fixes

• Fix ATTENTION registry (#729)
• Fix analyze log script (#716)
• Fix doc api display (#725)
• Fix patch_embed and pos_embed mismatch error (#685)
• Fix efficient test for multi-node (#707)
• Fix init_cfg in resnet backbone (#697)
• Fix efficient test bug (#702)
• Fix url error in config docs (#680)
• Fix mmcv installation (#676)
• Fix torch version (#670)

Contributors

@sshuair @xiexinch @Junjun2016 @mmeendez8 @xvjiarui @sennnnn @puhsu @BIGWangYuDong @keke1u @daavoo

V0.15 (07/04/2021)

Highlights

• Support ViT, SETR, and Swin-Transformer
• Add Chinese documentation
• Unified parameter initialization

Bug Fixes

• Fix typo and links (#608)
• Fix Dockerfile (#607)
• Fix ViT init (#609)
• Fix mmcv version compatible table (#658)
• Fix model links of DMNEt (#660)

New Features

• Support loading DeiT weights (#538)
• Support SETR (#531, #635)
• Add config and models for ViT backbone with UperHead (#520, #635)
• Support Swin-Transformer (#511)
• Add higher accuracy FastSCNN (#606)
• Add Chinese documentation (#666)

Improvements

• Unified parameter initialization (#567)
• Separate CUDA and CPU in github action CI (#602)
• Support persistent dataloader worker (#646)
• Update meta file fields (#661, #664)

V0.14 (06/02/2021)

Highlights

• Support ONNX to TensorRT
• Support MIM

Bug Fixes

• Fix ONNX to TensorRT verify (#547)
• Fix save best for EvalHook (#575)

New Features

• Support loading DeiT weights (#538)
• Support ONNX to TensorRT (#542)
• Support output results for ADE20k (#544)
• Support MIM (#549)

Improvements

• Add option for ViT output shape (#530)
• Infer batch size using len(result) (#532)
• Add compatible table between MMSeg and MMCV (#558)

V0.13 (05/05/2021)

Highlights

• Support Pascal Context Class-59 dataset.
• Support Visual Transformer Backbone.
• Support mFscore metric.

Bug Fixes

• Fixed Colaboratory tutorial (#451)
• Fixed mIoU calculation range (#471)
• Fixed sem_fpn, unet README.md (#492)
• Fixed num_classes in FCN for Pascal Context 60-class dataset (#488)
• Fixed FP16 inference (#497)

New Features

• Support dynamic export and visualize to pytorch2onnx (#463)
• Support export to torchscript (#469, #499)
• Support Pascal Context Class-59 dataset (#459)
• Support Visual Transformer backbone (#465)
• Support UpSample Neck (#512)
• Support mFscore metric (#509)

Improvements

• Add more CI for PyTorch (#460)
• Add print model graph args for tools/print_config.py (#451)
• Add cfg links in modelzoo README.md (#468)
• Add BaseSegmentor import to segmentors/init.py (#495)
• Add MMOCR, MMGeneration links (#501, #506)
• Add Chinese QR code (#506)
• Use MMCV MODEL_REGISTRY (#515)
• Add ONNX testing tools (#498)
• Replace data_dict calling 'img' key to support MMDet3D (#514)
• Support reading class_weight from file in loss function (#513)
• Make tags as comment (#505)
• Use MMCV EvalHook (#438)

V0.12 (04/03/2021)

Highlights

• Support FCN-Dilate 6 model.
• Support Dice Loss.

Bug Fixes

• Fixed PhotoMetricDistortion Doc (#388)
• Fixed install scripts (#399)
• Fixed Dice Loss multi-class (#417)

New Features

• Support Dice Loss (#396)
• Add plot logs tool (#426)
• Add opacity option to show_result (#425)
• Speed up mIoU metric (#430)

Improvements

• Refactor unittest file structure (#440)
• Fix typos in the repo (#449)
• Include class-level metrics in the log (#445)

V0.11 (02/02/2021)

Highlights

• Support memory efficient test, add more UNet models.

Bug Fixes

• Fixed TTA resize scale (#334)
• Fixed CI for pip 20.3 (#307)
• Fixed ADE20k test (#359)

New Features

• Support memory efficient test (#330)
• Add more UNet benchmarks (#324)
• Support Lovasz Loss (#351)

Improvements

• Move train_cfg/test_cfg inside model (#341)

V0.10 (01/01/2021)

Highlights

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

Bug Fixes

• Fixed CPU TTA (#276)
• Fixed CI for pip 20.3 (#307)

New Features

• Add ResNet18V1b, ResNet18V1c, ResNet50V1b, ResNet101V1b models (#316)
• Support MobileNetV3 (#268)
• Add 4 retinal vessel segmentation benchmark (#315)
• Support DMNet (#313)
• Support APCNet (#299)

Improvements

• Refactor Documentation page (#311)
• Support resize data augmentation according to original image size (#291)

V0.9 (30/11/2020)

Highlights

• Support 4 medical dataset, UNet and CGNet.

New Features

• Support RandomRotate transform (#215, #260)
• Support RGB2Gray transform (#227)
• Support Rerange transform (#228)
• Support ignore_index for BCE loss (#210)
• Add modelzoo statistics (#263)
• Support Dice evaluation metric (#225)
• Support Adjust Gamma transform (#232)
• Support CLAHE transform (#229)

Bug Fixes

• Fixed detail API link (#267)

V0.8 (03/11/2020)

Highlights

• Support 4 medical dataset, UNet and CGNet.

New Features

• Support customize runner (#118)
• Support UNet (#161)
• Support CHASE_DB1, DRIVE, STARE, HRD (#203)
• Support CGNet (#223)

V0.7 (07/10/2020)

Highlights

• Support Pascal Context dataset and customizing class dataset.

Bug Fixes

• Fixed CPU inference (#153)

New Features

• Add DeepLab OS16 models (#154)
• Support Pascal Context dataset (#133)
• Support customizing dataset classes (#71)
• Support customizing dataset palette (#157)

Improvements

• Support 4D tensor output in ONNX (#150)
• Remove redundancies in ONNX export (#160)
• Migrate to MMCV DepthwiseSeparableConv (#158)
• Migrate to MMCV collect_env (#137)
• Use img_prefix and seg_prefix for loading (#153)

V0.6 (10/09/2020)

Highlights

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

Bug Fixes

• Fixed sliding inference ONNX export (#90)

New Features

• Support MobileNet v2 (#86)
• Support EMANet (#34)
• Support DNL (#37)
• Support PointRend (#109)
• Support Semantic FPN (#94)
• Support Fast-SCNN (#58)
• Support ResNeSt backbone (#47)
• Support ONNX export (experimental) (#12)

Improvements

• Support Upsample in ONNX (#100)
• Support Windows install (experimental) (#75)
• Add more OCRNet results (#20)
• Add PyTorch 1.6 CI (#64)
• Get version and githash automatically (#55)

v0.5.1 (11/08/2020)

Highlights

• Support FP16 and more generalized OHEM

Bug Fixes

• Fixed Pascal VOC conversion script (#19)
• Fixed OHEM weight assign bug (#54)
• Fixed palette type when palette is not given (#27)

New Features

• Support FP16 (#21)
• Generalized OHEM (#54)

Improvements

• Add load-from flag (#33)
• Fixed training tricks doc about different learning rates of model (#26)