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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

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

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

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)

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