Changelog¶

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  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)