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v2.25.1 (29/7/2022)

Bug Fixes

  • Fix single GPU distributed training of cuda device specifying (#8176)

  • Fix PolygonMask bug in FilterAnnotations (#8136)

  • Fix mdformat version to support python3.6 (#8195)

  • Fix GPG key error in Dockerfile (#8215)

  • Fix WandbLoggerHook error (#8273)

  • Fix Pytorch 1.10 incompatibility issues (#8439)

Improvements

  • Add mim to extras_require in setup.py (#8194)

  • Support get image shape on macOS (#8434)

  • Add test commands of mim in CI (#8230 & #8240)

  • Update maskformer to be compatible when cfg is a dictionary (#8263)

  • Clean Pillow version check in CI (#8229)

Documents

  • Change example hook name in tutorials (#8118)

  • Update projects (#8120)

  • Update metafile and release new models (#8294)

  • Add download link in tutorials (#8391)

Contributors

A total of 15 developers contributed to this release. Thanks @ZwwWayne, @ayulockin, @Mxbonn, @p-mishra1, @Youth-Got, @MiXaiLL76, @chhluo, @jbwang1997, @atinfinity, @shinya7y, @duanzhihua, @STLAND-admin, @BIGWangYuDong, @grimoire, @xiaoyuan0203

v2.25.0 (31/5/2022)

Highlights

  • Support dedicated WandbLogger hook

  • Support ConvNeXt, DDOD, SOLOv2

  • Support Mask2Former for instance segmentation

  • Rename config files of Mask2Former

Backwards incompatible changes

  • Rename config files of Mask2Former (#7571)

    before v2.25.0 after v2.25.0
    • mask2former_xxx_coco.py represents config files for panoptic segmentation.

    • mask2former_xxx_coco.py represents config files for instance segmentation.

    • mask2former_xxx_coco-panoptic.py represents config files for panoptic segmentation.

New Features

Bug Fixes

  • Enable YOLOX training on different devices (#7912)

  • Fix the log plot error when evaluation with interval != 1 (#7784)

  • Fix RuntimeError of HTC (#8083)

Improvements

  • Support dedicated WandbLogger hook (#7459)

    Users can set

    cfg.log_config.hooks = [
      dict(type='MMDetWandbHook',
           init_kwargs={'project': 'MMDetection-tutorial'},
           interval=10,
           log_checkpoint=True,
           log_checkpoint_metadata=True,
           num_eval_images=10)]
    

    in the config to use MMDetWandbHook. Example can be found in this colab tutorial

  • Add AvoidOOM to avoid OOM (#7434, #8091)

    Try to use AvoidCUDAOOM to avoid GPU out of memory. It will first retry after calling torch.cuda.empty_cache(). If it still fails, it will then retry by converting the type of inputs to FP16 format. If it still fails, it will try to copy inputs from GPUs to CPUs to continue computing. Try AvoidOOM in code to make the code continue to run when GPU memory runs out:

    from mmdet.utils import AvoidCUDAOOM
    
    output = AvoidCUDAOOM.retry_if_cuda_oom(some_function)(input1, input2)
    

    Users can also try AvoidCUDAOOM as a decorator to make the code continue to run when GPU memory runs out:

    from mmdet.utils import AvoidCUDAOOM
    
    @AvoidCUDAOOM.retry_if_cuda_oom
    def function(*args, **kwargs):
        ...
        return xxx
    
  • Support reading gpu_collect from cfg.evaluation.gpu_collect (#7672)

  • Speedup the Video Inference by Accelerating data-loading Stage (#7832)

  • Support replacing the ${key} with the value of cfg.key (#7492)

  • Accelerate result analysis in analyze_result.py. The evaluation time is speedup by 10 ~ 15 times and only tasks 10 ~ 15 minutes now. (#7891)

  • Support to set block_dilations in DilatedEncoder (#7812)

  • Support panoptic segmentation result analysis (#7922)

  • Release DyHead with Swin-Large backbone (#7733)

  • Documentations updating and adding

    • Fix wrong default type of act_cfg in SwinTransformer (#7794)

    • Fix text errors in the tutorials (#7959)

    • Rewrite the installation guide (#7897)

    • Useful hooks (#7810)

    • Fix heading anchor in documentation (#8006)

    • Replace markdownlint with mdformat for avoiding installing ruby (#8009)

Contributors

A total of 20 developers contributed to this release.

Thanks @ZwwWayne, @DarthThomas, @solyaH, @LutingWang, @chenxinfeng4, @Czm369, @Chenastron, @chhluo, @austinmw, @Shanyaliux @hellock, @Y-M-Y, @jbwang1997, @hhaAndroid, @Irvingao, @zhanggefan, @BIGWangYuDong, @Keiku, @PeterVennerstrom, @ayulockin

v2.24.0 (26/4/2022)

Highlights

New Features

  • Support Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation, see example configs (#7501)

  • Support Class Aware Sampler, users can set

    data=dict(train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1))))
    

    in the config to use ClassAwareSampler. Examples can be found in the configs of OpenImages Dataset. (#7436)

  • Support automatically scaling LR according to GPU number and samples per GPU. (#7482) In each config, there is a corresponding config of auto-scaling LR as below,

    auto_scale_lr = dict(enable=True, base_batch_size=N)
    

    where N is the batch size used for the current learning rate in the config (also equals to samples_per_gpu * gpu number to train this config). By default, we set enable=False so that the original usages will not be affected. Users can set enable=True in each config or add --auto-scale-lr after the command line to enable this feature and should check the correctness of base_batch_size in customized configs.

  • Support setting dataloader arguments in config and add functions to handle config compatibility. (#7668) The comparison between the old and new usages is as below.

    v2.23.0 v2.24.0
    data = dict(
        samples_per_gpu=64, workers_per_gpu=4,
        train=dict(type='xxx', ...),
        val=dict(type='xxx', samples_per_gpu=4, ...),
        test=dict(type='xxx', ...),
    )
    
    # A recommended config that is clear
    data = dict(
        train=dict(type='xxx', ...),
        val=dict(type='xxx', ...),
        test=dict(type='xxx', ...),
        # Use different batch size during inference.
        train_dataloader=dict(samples_per_gpu=64, workers_per_gpu=4),
        val_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
        test_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
    )
    
    # Old style still works but allows to set more arguments about data loaders
    data = dict(
        samples_per_gpu=64,  # only works for train_dataloader
        workers_per_gpu=4,  # only works for train_dataloader
        train=dict(type='xxx', ...),
        val=dict(type='xxx', ...),
        test=dict(type='xxx', ...),
        # Use different batch size during inference.
        val_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
        test_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
    )
    
  • Support memory profile hook. Users can use it to monitor the memory usages during training as below (#7560)

    custom_hooks = [
        dict(type='MemoryProfilerHook', interval=50)
    ]
    
  • Support to run on PyTorch with MLU chip (#7578)

  • Support re-spliting data batch with tag (#7641)

  • Support the DiceCost used by K-Net in MaskHungarianAssigner (#7716)

  • Support splitting COCO data for Semi-supervised object detection (#7431)

  • Support Pathlib for Config.fromfile (#7685)

  • Support to use file client in OpenImages dataset (#7433)

  • Add a probability parameter to Mosaic transformation (#7371)

  • Support specifying interpolation mode in Resize pipeline (#7585)

Bug Fixes

  • Avoid invalid bbox after deform_sampling (#7567)

  • Fix the issue that argument color_theme does not take effect when exporting confusion matrix (#7701)

  • Fix the end_level in Necks, which should be the index of the end input backbone level (#7502)

  • Fix the bug that mix_results may be None in MultiImageMixDataset (#7530)

  • Fix the bug in ResNet plugin when two plugins are used (#7797)

Improvements

  • Enhance load_json_logs of analyze_logs.py for resumed training logs (#7732)

  • Add argument out_file in image_demo.py (#7676)

  • Allow mixed precision training with SimOTAAssigner (#7516)

  • Updated INF to 100000.0 to be the same as that in the official YOLOX (#7778)

  • Add documentations of:

    • how to get channels of a new backbone (#7642)

    • how to unfreeze the backbone network (#7570)

    • how to train fast_rcnn model (#7549)

    • proposals in Deformable DETR (#7690)

    • from-scratch install script in get_started.md (#7575)

  • Release pre-trained models of

    • Mask2Former (#7595, #7709)

    • RetinaNet with ResNet-18 and release models (#7387)

    • RetinaNet with EfficientNet backbone (#7646)

Contributors

A total of 27 developers contributed to this release. Thanks @jovialio, @zhangsanfeng2022, @HarryZJ, @jamiechoi1995, @nestiank, @PeterH0323, @RangeKing, @Y-M-Y, @mattcasey02, @weiji14, @Yulv-git, @xiefeifeihu, @FANG-MING, @meng976537406, @nijkah, @sudz123, @CCODING04, @SheffieldCao, @Czm369, @BIGWangYuDong, @zytx121, @jbwang1997, @chhluo, @jshilong, @RangiLyu, @hhaAndroid, @ZwwWayne

v2.23.0 (28/3/2022)

Highlights

New Features

  • Support Mask2Former(#6938)(#7466)(#7471)

  • Support EfficientNet (#7514)

  • Support setting data root through environment variable MMDET_DATASETS, users don’t have to modify the corresponding path in config files anymore. (#7386)

  • Support setting different seeds to different ranks (#7432)

  • Update the dist_train.sh so that the script can be used to support launching multi-node training on machines without slurm (#7415)

  • Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision (#7489)

Bug Fixes

  • Fix bug in VOC unit test which removes the data directory (#7270)

  • Adjust the order of get_classes and FileClient (#7276)

  • Force the inputs of get_bboxes in yolox_head to float32 (#7324)

  • Fix misplaced arguments in LoadPanopticAnnotations (#7388)

  • Fix reduction=mean in CELoss. (#7449)

  • Update unit test of CrossEntropyCost (#7537)

  • Fix memory leaking in panpotic segmentation evaluation (#7538)

  • Fix the bug of shape broadcast in YOLOv3 (#7551)

Improvements

  • Add Chinese version of onnx2tensorrt.md (#7219)

  • Update colab tutorials (#7310)

  • Update information about Localization Distillation (#7350)

  • Add Chinese version of finetune.md (#7178)

  • Update YOLOX log for non square input (#7235)

  • Add nproc in coco_panoptic.py for panoptic quality computing (#7315)

  • Allow to set channel_order in LoadImageFromFile (#7258)

  • Take point sample related functions out of mask_point_head (#7353)

  • Add instance evaluation for coco_panoptic (#7313)

  • Enhance the robustness of analyze_logs.py (#7407)

  • Supplementary notes of sync_random_seed (#7440)

  • Update docstring of cross entropy loss (#7472)

  • Update pascal voc result (#7503)

  • We create How-to documentation to record any questions about How to xxx. In this version, we added

    • How to use Mosaic augmentation (#7507)

    • How to use backbone in mmcls (#7438)

    • How to produce and submit the prediction results of panoptic segmentation models on COCO test-dev set (#7430))

Contributors

A total of 27 developers contributed to this release. Thanks @ZwwWayne, @haofanwang, @shinya7y, @chhluo, @yangrisheng, @triple-Mu, @jbwang1997, @HikariTJU, @imflash217, @274869388, @zytx121, @matrixgame2018, @jamiechoi1995, @BIGWangYuDong, @JingweiZhang12, @Xiangxu-0103, @hhaAndroid, @jshilong, @osbm, @ceroytres, @bunge-bedstraw-herb, @Youth-Got, @daavoo, @jiangyitong, @RangiLyu, @CCODING04, @yarkable

v2.22.0 (24/2/2022)

Highlights

New Features

  • Support MaskFormer (#7212)

  • Support DyHead (#6823)

  • Support ResNet Strikes Back (#7001)

  • Support OpenImages Dataset (#6331)

  • Support TIMM backbone (#7020)

  • Support visualization for Panoptic Segmentation (#7041)

Breaking Changes

In order to support the visualization for Panoptic Segmentation, the num_classes can not be None when using the get_palette function to determine whether to use the panoptic palette.

Bug Fixes

  • Fix bug for the best checkpoints can not be saved when the key_score is None (#7101)

  • Fix MixUp transform filter boxes failing case (#7080)

  • Add missing properties in SABLHead (#7091)

  • Fix bug when NaNs exist in confusion matrix (#7147)

  • Fix PALETTE AttributeError in downstream task (#7230)

Improvements

  • Speed up SimOTA matching (#7098)

  • Add Chinese translation of docs_zh-CN/tutorials/init_cfg.md (#7188)

Contributors

A total of 20 developers contributed to this release. Thanks @ZwwWayne, @hhaAndroid, @RangiLyu, @AronLin, @BIGWangYuDong, @jbwang1997, @zytx121, @chhluo, @shinya7y, @LuooChen, @dvansa, @siatwangmin, @del-zhenwu, @vikashranjan26, @haofanwang, @jamiechoi1995, @HJoonKwon, @yarkable, @zhijian-liu, @RangeKing

v2.21.0 (8/2/2022)

Breaking Changes

To standardize the contents in config READMEs and meta files of OpenMMLab projects, the READMEs and meta files in each config directory have been significantly changed. The template will be released in the future, for now, you can refer to the examples of README for algorithm, dataset and backbone. To align with the standard, the configs in dcn are put into to two directories named dcn and dcnv2.

New Features

  • Allow to customize colors of different classes during visualization (#6716)

  • Support CPU training (#7016)

  • Add download script of COCO, LVIS, and VOC dataset (#7015)

Bug Fixes

  • Fix weight conversion issue of RetinaNet with Swin-S (#6973)

  • Update __repr__ of Compose (#6951)

  • Fix BadZipFile Error when build docker (#6966)

  • Fix bug in non-distributed multi-gpu training/testing (#7019)

  • Fix bbox clamp in PyTorch 1.10 (#7074)

  • Relax the requirement of PALETTE in dataset wrappers (#7085)

  • Keep the same weights before reassign in the PAA head (#7032)

  • Update code demo in doc (#7092)

Improvements

  • Speed-up training by allow to set variables of multi-processing (#6974, #7036)

  • Add links of Chinese tutorials in readme (#6897)

  • Disable cv2 multiprocessing by default for acceleration (#6867)

  • Deprecate the support for “python setup.py test” (#6998)

  • Re-organize metafiles and config readmes (#7051)

  • Fix None grad problem during training TOOD by adding SigmoidGeometricMean (#7090)

Contributors

A total of 26 developers contributed to this release. Thanks @del-zhenwu, @zimoqingfeng, @srishilesh, @imyhxy, @jenhaoyang, @jliu-ac, @kimnamu, @ShengliLiu, @garvan2021, @ciusji, @DIYer22, @kimnamu, @q3394101, @zhouzaida, @gaotongxiao, @topsy404, @AntoAndGar, @jbwang1997, @nijkah, @ZwwWayne, @Czm369, @jshilong, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @AronLin

v2.20.0 (27/12/2021)

New Features

  • Support TOOD: Task-aligned One-stage Object Detection (ICCV 2021 Oral) (#6746)

  • Support resuming from the latest checkpoint automatically (#6727)

Bug Fixes

  • Fix wrong bbox loss_weight of the PAA head (#6744)

  • Fix the padding value of gt_semantic_seg in batch collating (#6837)

  • Fix test error of lvis when using classwise (#6845)

  • Avoid BC-breaking of get_local_path (#6719)

  • Fix bug in sync_norm_hook when the BN layer does not exist (#6852)

  • Use pycocotools directly no matter what platform it is (#6838)

Improvements

  • Add unit test for SimOTA with no valid bbox (#6770)

  • Use precommit to check readme (#6802)

  • Support selecting GPU-ids in non-distributed testing time (#6781)

Contributors

A total of 16 developers contributed to this release. Thanks @ZwwWayne, @Czm369, @jshilong, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @jamiechoi1995, @AronLin, @Keiku, @gkagkos, @fcakyon, @www516717402, @vansin, @zactodd, @kimnamu, @jenhaoyang

v2.19.1 (14/12/2021)

New Features

  • Release YOLOX COCO pretrained models (#6698)

Bug Fixes

  • Fix DCN initialization in DenseHead (#6625)

  • Fix initialization of ConvFCHead (#6624)

  • Fix PseudoSampler in RCNN (#6622)

  • Fix weight initialization in Swin and PVT (#6663)

  • Fix dtype bug in BaseDenseHead (#6767)

  • Fix SimOTA with no valid bbox (#6733)

Improvements

  • Add an example of combining swin and one-stage models (#6621)

  • Add get_ann_info to dataset_wrappers (#6526)

  • Support keeping image ratio in the multi-scale training of YOLOX (#6732)

  • Support bbox_clip_border for the augmentations of YOLOX (#6730)

Documents

  • Update metafile (#6717)

  • Add mmhuman3d in readme (#6699)

  • Update FAQ docs (#6587)

  • Add doc for detect_anomalous_params (#6697)

Contributors

A total of 11 developers contributed to this release. Thanks @ZwwWayne, @LJoson, @Czm369, @jshilong, @ZCMax, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @zhaoxin111, @GT9505, @shinya7y

v2.19.0 (29/11/2021)

Highlights

New Features

Bug Fixes

  • Fix repeatedly output warning message (#6584)

  • Avoid infinite GPU waiting in dist training (#6501)

  • Fix SSD512 config error (#6574)

  • Fix MMDetection model to ONNX command (#6558)

Improvements

  • Refactor configs of FP16 models (#6592)

  • Align accuracy to the updated official YOLOX (#6443)

  • Speed up training and reduce memory cost when using PhotoMetricDistortion. (#6442)

  • Make OHEM work with seesaw loss (#6514)

Documents

  • Update README.md (#6567)

Contributors

A total of 11 developers contributed to this release. Thanks @FloydHsiu, @RangiLyu, @ZwwWayne, @AndreaPi, @st9007a, @hachreak, @BIGWangYuDong, @hhaAndroid, @AronLin, @chhluo, @vealocia, @HarborYuan, @st9007a, @jshilong

v2.18.1 (15/11/2021)

Highlights

  • Release QueryInst pre-trained weights (#6460)

  • Support plot confusion matrix (#6344)

New Features

  • Release QueryInst pre-trained weights (#6460)

  • Support plot confusion matrix (#6344)

Bug Fixes

  • Fix aug test error when the number of prediction bboxes is 0 (#6398)

  • Fix SpatialReductionAttention in PVT (#6488)

  • Fix wrong use of trunc_normal_init in PVT and Swin-Transformer (#6432)

Improvements

  • Save the printed AP information of COCO API to logger (#6505)

  • Always map location to cpu when load checkpoint (#6405)

  • Set a random seed when the user does not set a seed (#6457)

Documents

Contributors

A total of 11 developers contributed to this release. Thanks @st9007a, @hachreak, @HarborYuan, @vealocia, @chhluo, @AndreaPi, @AronLin, @BIGWangYuDong, @hhaAndroid, @RangiLyu, @ZwwWayne

v2.18.0 (27/10/2021)

Highlights

  • Support QueryInst (#6050)

  • Refactor dense heads to decouple onnx export logics from get_bboxes and speed up inference (#5317, #6003, #6369, #6268, #6315)

New Features

  • Support QueryInst (#6050)

  • Support infinite sampler (#5996)

Bug Fixes

  • Fix init_weight in fcn_mask_head (#6378)

  • Fix type error in imshow_bboxes of RPN (#6386)

  • Fix broken colab link in MMDetection Tutorial (#6382)

  • Make sure the device and dtype of scale_factor are the same as bboxes (#6374)

  • Remove sampling hardcode (#6317)

  • Fix RandomAffine bbox coordinate recorrection (#6293)

  • Fix init bug of final cls/reg layer in convfc head (#6279)

  • Fix img_shape broken in auto_augment (#6259)

  • Fix kwargs parameter missing error in two_stage (#6256)

Improvements

  • Unify the interface of stuff head and panoptic head (#6308)

  • Polish readme (#6243)

  • Add code-spell pre-commit hook and fix a typo (#6306)

  • Fix typo (#6245, #6190)

  • Fix sampler unit test (#6284)

  • Fix forward_dummy of YOLACT to enable get_flops (#6079)

  • Fix link error in the config documentation (#6252)

  • Adjust the order to beautify the document (#6195)

Refactors

  • Refactor one-stage get_bboxes logic (#5317)

  • Refactor ONNX export of One-Stage models (#6003, #6369)

  • Refactor dense_head and speedup (#6268)

  • Migrate to use prior_generator in training of dense heads (#6315)

Contributors

A total of 18 developers contributed to this release. Thanks @Boyden, @onnkeat, @st9007a, @vealocia, @yhcao6, @DapangpangX, @yellowdolphin, @cclauss, @kennymckormick, @pingguokiller, @collinzrj, @AndreaPi, @AronLin, @BIGWangYuDong, @hhaAndroid, @jshilong, @RangiLyu, @ZwwWayne

v2.17.0 (28/9/2021)

Highlights

  • Support PVT and PVTv2

  • Support SOLO

  • Support large scale jittering and New Mask R-CNN baselines

  • Speed up YOLOv3 inference

New Features

  • Support PVT and PVTv2 (#5780)

  • Support SOLO (#5832)

  • Support large scale jittering and New Mask R-CNN baselines (#6132)

  • Add a general data structrue for the results of models (#5508)

  • Added a base class for one-stage instance segmentation (#5904)

  • Speed up YOLOv3 inference (#5991)

  • Release Swin Transformer pre-trained models (#6100)

  • Support mixed precision training in YOLOX (#5983)

  • Support val workflow in YOLACT (#5986)

  • Add script to test torchserve (#5936)

  • Support onnxsim with dynamic input shape (#6117)

Bug Fixes

  • Fix the function naming errors in model_wrappers (#5975)

  • Fix regression loss bug when the input is an empty tensor (#5976)

  • Fix scores not contiguous error in centernet_head (#6016)

  • Fix missing parameters bug in imshow_bboxes (#6034)

  • Fix bug in aug_test of HTC when the length of det_bboxes is 0 (#6088)

  • Fix empty proposal errors in the training of some two-stage models (#5941)

  • Fix dynamic_axes parameter error in ONNX dynamic shape export (#6104)

  • Fix dynamic_shape bug of SyncRandomSizeHook (#6144)

  • Fix the Swin Transformer config link error in the configuration (#6172)

Improvements

  • Add filter rules in Mosaic transform (#5897)

  • Add size divisor in get flops to avoid some potential bugs (#6076)

  • Add Chinese translation of docs_zh-CN/tutorials/customize_dataset.md (#5915)

  • Add Chinese translation of conventions.md (#5825)

  • Add description of the output of data pipeline (#5886)

  • Add dataset information in the README file for PanopticFPN (#5996)

  • Add extra_repr for DropBlock layer to get details in the model printing (#6140)

  • Fix CI out of memory and add PyTorch1.9 Python3.9 unit tests (#5862)

  • Fix download links error of some model (#6069)

  • Improve the generalization of XML dataset (#5943)

  • Polish assertion error messages (#6017)

  • Remove opencv-python-headless dependency by albumentations (#5868)

  • Check dtype in transform unit tests (#5969)

  • Replace the default theme of documentation with PyTorch Sphinx Theme (#6146)

  • Update the paper and code fields in the metafile (#6043)

  • Support to customize padding value of segmentation map (#6152)

  • Support to resize multiple segmentation maps (#5747)

Contributors

A total of 24 developers contributed to this release. Thanks @morkovka1337, @HarborYuan, @guillaumefrd, @guigarfr, @www516717402, @gaotongxiao, @ypwhs, @MartaYang, @shinya7y, @justiceeem, @zhaojinjian0000, @VVsssssk, @aravind-anantha, @wangbo-zhao, @czczup, @whai362, @czczup, @marijnl, @AronLin, @BIGWangYuDong, @hhaAndroid, @jshilong, @RangiLyu, @ZwwWayne

v2.16.0 (30/8/2021)

Highlights

New Features

  • Support Panoptic FPN and release models (#5577, #5902)

  • Support Swin Transformer backbone (#5748)

  • Release RetinaNet models pre-trained with multi-scale 3x schedule (#5636)

  • Add script to convert unlabeled image list to coco format (#5643)

  • Add hook to check whether the loss value is valid (#5674)

  • Add YOLO anchor optimizing tool (#5644)

  • Support export onnx models without post process. (#5851)

  • Support classwise evaluation in CocoPanopticDataset (#5896)

  • Adapt browse_dataset for concatenated datasets. (#5935)

  • Add PatchEmbed and PatchMerging with AdaptivePadding (#5952)

Bug Fixes

  • Fix unit tests of YOLOX (#5859)

  • Fix lose randomness in imshow_det_bboxes (#5845)

  • Make output result of ImageToTensor contiguous (#5756)

  • Fix inference bug when calling regress_by_class in RoIHead in some cases (#5884)

  • Fix bug in CIoU loss where alpha should not have gradient. (#5835)

  • Fix the bug that multiscale_output is defined but not used in HRNet (#5887)

  • Set the priority of EvalHook to LOW. (#5882)

  • Fix a YOLOX bug when applying bbox rescaling in test mode (#5899)

  • Fix mosaic coordinate error (#5947)

  • Fix dtype of bbox in RandomAffine. (#5930)

Improvements

  • Add Chinese version of data_pipeline and (#5662)

  • Support to remove state dicts of EMA when publishing models. (#5858)

  • Refactor the loss function in HTC and SCNet (#5881)

  • Use warnings instead of logger.warning (#5540)

  • Use legacy coordinate in metric of VOC (#5627)

  • Add Chinese version of customize_losses (#5826)

  • Add Chinese version of model_zoo (#5827)

Contributors

A total of 19 developers contributed to this release. Thanks @ypwhs, @zywvvd, @collinzrj, @OceanPang, @ddonatien, @@haotian-liu, @viibridges, @Muyun99, @guigarfr, @zhaojinjian0000, @jbwang1997,@wangbo-zhao, @xvjiarui, @RangiLyu, @jshilong, @AronLin, @BIGWangYuDong, @hhaAndroid, @ZwwWayne

v2.15.1 (11/8/2021)

Highlights

New Features

  • Support YOLOX(#5756, #5758, #5760, #5767, #5770, #5774, #5777, #5808, #5828, #5848)

Bug Fixes

  • Update correct SSD models. (#5789)

  • Fix casting error in mask structure (#5820)

  • Fix MMCV deployment documentation links. (#5790)

Improvements

  • Use dynamic MMCV download link in TorchServe dockerfile (#5779)

  • Rename the function upsample_like to interpolate_as for more general usage (#5788)

Contributors

A total of 14 developers contributed to this release. Thanks @HAOCHENYE, @xiaohu2015, @HsLOL, @zhiqwang, @Adamdad, @shinya7y, @Johnson-Wang, @RangiLyu, @jshilong, @mmeendez8, @AronLin, @BIGWangYuDong, @hhaAndroid, @ZwwWayne

v2.15.0 (02/8/2021)

Highlights

  • Support adding MIM dependencies during pip installation

  • Support MobileNetV2 for SSD-Lite and YOLOv3

  • Support Chinese Documentation

New Features

  • Add function upsample_like (#5732)

  • Support to output pdf and epub format documentation (#5738)

  • Support and release Cascade Mask R-CNN 3x pre-trained models (#5645)

  • Add ignore_index to CrossEntropyLoss (#5646)

  • Support adding MIM dependencies during pip installation (#5676)

  • Add MobileNetV2 config and models for YOLOv3 (#5510)

  • Support COCO Panoptic Dataset (#5231)

  • Support ONNX export of cascade models (#5486)

  • Support DropBlock with RetinaNet (#5544)

  • Support MobileNetV2 SSD-Lite (#5526)

Bug Fixes

  • Fix the device of label in multiclass_nms (#5673)

  • Fix error of backbone initialization from pre-trained checkpoint in config file (#5603, #5550)

  • Fix download links of RegNet pretrained weights (#5655)

  • Fix two-stage runtime error given empty proposal (#5559)

  • Fix flops count error in DETR (#5654)

  • Fix unittest for NumClassCheckHook when it is not used. (#5626)

  • Fix description bug of using custom dataset (#5546)

  • Fix bug of multiclass_nms that returns the global indices (#5592)

  • Fix valid_mask logic error in RPNHead (#5562)

  • Fix unit test error of pretrained configs (#5561)

  • Fix typo error in anchor_head.py (#5555)

  • Fix bug when using dataset wrappers (#5552)

  • Fix a typo error in demo/MMDet_Tutorial.ipynb (#5511)

  • Fixing crash in get_root_logger when cfg.log_level is not None (#5521)

  • Fix docker version (#5502)

  • Fix optimizer parameter error when using IterBasedRunner (#5490)

Improvements

  • Add unit tests for MMTracking (#5620)

  • Add Chinese translation of documentation (#5718, #5618, #5558, #5423, #5593, #5421, #5408. #5369, #5419, #5530, #5531)

  • Update resource limit (#5697)

  • Update docstring for InstaBoost (#5640)

  • Support key reduction_override in all loss functions (#5515)

  • Use repeatdataset to accelerate CenterNet training (#5509)

  • Remove unnecessary code in autoassign (#5519)

  • Add documentation about init_cfg (#5273)

Contributors

A total of 18 developers contributed to this release. Thanks @OceanPang, @AronLin, @hellock, @Outsider565, @RangiLyu, @ElectronicElephant, @likyoo, @BIGWangYuDong, @hhaAndroid, @noobying, @yyz561, @likyoo, @zeakey, @ZwwWayne, @ChenyangLiu, @johnson-magic, @qingswu, @BuxianChen

v2.14.0 (29/6/2021)

Highlights

  • Add simple_test to dense heads to improve the consistency of single-stage and two-stage detectors

  • Revert the test_mixins to single image test to improve efficiency and readability

  • Add Faster R-CNN and Mask R-CNN config using multi-scale training with 3x schedule

New Features

  • Support pretrained models from MoCo v2 and SwAV (#5286)

  • Add Faster R-CNN and Mask R-CNN config using multi-scale training with 3x schedule (#5179, #5233)

  • Add reduction_override in MSELoss (#5437)

  • Stable support of exporting DETR to ONNX with dynamic shapes and batch inference (#5168)

  • Stable support of exporting PointRend to ONNX with dynamic shapes and batch inference (#5440)

Bug Fixes

  • Fix size mismatch bug in multiclass_nms (#4980)

  • Fix the import path of MultiScaleDeformableAttention (#5338)

  • Fix errors in config of GCNet ResNext101 models (#5360)

  • Fix Grid-RCNN error when there is no bbox result (#5357)

  • Fix errors in onnx_export of bbox_head when setting reg_class_agnostic (#5468)

  • Fix type error of AutoAssign in the document (#5478)

  • Fix web links ending with .md (#5315)

Improvements

  • Add simple_test to dense heads to improve the consistency of single-stage and two-stage detectors (#5264)

  • Add support for mask diagonal flip in TTA (#5403)

  • Revert the test_mixins to single image test to improve efficiency and readability (#5249)

  • Make YOLOv3 Neck more flexible (#5218)

  • Refactor SSD to make it more general (#5291)

  • Refactor anchor_generator and point_generator (#5349)

  • Allow to configure out the mask_head of the HTC algorithm (#5389)

  • Delete deprecated warning in FPN (#5311)

  • Move model.pretrained to model.backbone.init_cfg (#5370)

  • Make deployment tools more friendly to use (#5280)

  • Clarify installation documentation (#5316)

  • Add ImageNet Pretrained Models docs (#5268)

  • Add FAQ about training loss=nan solution and COCO AP or AR =-1 (# 5312, #5313)

  • Change all weight links of http to https (#5328)

v2.13.0 (01/6/2021)

Highlights

New Features

Bug Fixes

  • Fix YOLOv3 FP16 training error (#5172)

  • Fix Cacscade R-CNN TTA test error when det_bboxes length is 0 (#5221)

  • Fix iou_thr variable naming errors in VOC recall calculation function (#5195)

  • Fix Faster R-CNN performance dropped in ONNX Runtime (#5197)

  • Fix DETR dict changed error when using python 3.8 during iteration (#5226)

Improvements

  • Refactor ONNX export of two stage detector (#5205)

  • Replace MMDetection’s EvalHook with MMCV’s EvalHook for consistency (#4806)

  • Update RoI extractor for ONNX (#5194)

  • Use better parameter initialization in YOLOv3 head for higher performance (#5181)

  • Release new DCN models of Mask R-CNN by mixed-precision training (#5201)

  • Update YOLOv3 model weights (#5229)

  • Add DetectoRS ResNet-101 model weights (#4960)

  • Discard bboxes with sizes equals to min_bbox_size (#5011)

  • Remove duplicated code in DETR head (#5129)

  • Remove unnecessary object in class definition (#5180)

  • Fix doc link (#5192)

v2.12.0 (01/5/2021)

Highlights

  • Support new methods: AutoAssign, YOLOF, and Deformable DETR

  • Stable support of exporting models to ONNX with batched images and dynamic shape (#5039)

Backwards Incompatible Changes

MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.15.0 (maybe longer). In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation.

  • MMCV version. MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including BaseModule for unified parameter initialization, model registry, and the CUDA operator MultiScaleDeformableAttn for Deformable DETR. Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users skip MMCV v1.3.2 and use v1.3.3, though v1.3.2 might work for most cases.

  • Unified model initialization (#4750). To unify the parameter initialization in OpenMMLab projects, MMCV supports BaseModule that accepts init_cfg to allow the modules’ parameters initialized in a flexible and unified manner. Now the users need to explicitly call model.init_weights() in the training script to initialize the model (as in here, previously this was handled by the detector. The models in MMDetection have been re-benchmarked to ensure accuracy based on PR #4750. The downstream projects should update their code accordingly to use MMDetection v2.12.0.

  • Unified model registry (#5059). To easily use backbones implemented in other OpenMMLab projects, MMDetection migrates to inherit the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection.

  • Mask AP evaluation (#4898). Previous versions calculate the areas of masks through the bounding boxes when calculating the mask AP of small, medium, and large instances. To indeed use the areas of masks, we pop the key bbox during mask AP calculation. This change does not affect the overall mask AP evaluation and aligns the mask AP of similar models in other projects like Detectron2.

New Features

Improvements

  • Use MMCV MODEL_REGISTRY (#5059)

  • Unified parameter initialization for more flexible usage (#4750)

  • Rename variable names and fix docstring in anchor head (#4883)

  • Support training with empty GT in Cascade RPN (#4928)

  • Add more details of usage of test_robustness in documentation (#4917)

  • Changing to use pycocotools instead of mmpycocotools to fully support Detectron2 and MMDetection in one environment (#4939)

  • Update torch serve dockerfile to support dockers of more versions (#4954)

  • Add check for training with single class dataset (#4973)

  • Refactor transformer and DETR Head (#4763)

  • Update FPG model zoo (#5079)

  • More accurate mask AP of small/medium/large instances (#4898)

Bug Fixes

  • Fix bug in mean_ap.py when calculating mAP by 11 points (#4875)

  • Fix error when key meta is not in old checkpoints (#4936)

  • Fix hanging bug when training with empty GT in VFNet, GFL, and FCOS by changing the place of reduce_mean (#4923, #4978, #5058)

  • Fix asyncronized inference error and provide related demo (#4941)

  • Fix IoU losses dimensionality unmatch error (#4982)

  • Fix torch.randperm whtn using PyTorch 1.8 (#5014)

  • Fix empty bbox error in mask_head when using CARAFE (#5062)

  • Fix supplement_mask bug when there are zero-size RoIs (#5065)

  • Fix testing with empty rois in RoI Heads (#5081)

v2.11.0 (01/4/2021)

Highlights

New Features

Improvements

  • Support batch inference in head of RetinaNet (#4699)

  • Add batch dimension in second stage of Faster-RCNN (#4785)

  • Support batch inference in bbox coder (#4721)

  • Add check for ann_ids in COCODataset to ensure it is unique (#4789)

  • support for showing the FPN results (#4716)

  • support dynamic shape for grid_anchor (#4684)

  • Move pycocotools version check to when it is used (#4880)

Bug Fixes

  • Fix a bug of TridentNet when doing the batch inference (#4717)

  • Fix a bug of Pytorch2ONNX in FASF (#4735)

  • Fix a bug when show the image with float type (#4732)

v2.10.0 (01/03/2021)

Highlights

  • Support new methods: FPG

  • Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN.

New Features

  • Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN (#4569)

  • Support Feature Pyramid Grids (FPG) (#4645)

  • Support video demo (#4420)

  • Add seed option for sampler (#4665)

  • Support to customize type of runner (#4570, #4669)

  • Support synchronizing BN buffer in EvalHook (#4582)

  • Add script for GIF demo (#4573)

Bug Fixes

  • Fix ConfigDict AttributeError and add Colab link (#4643)

  • Avoid crash in empty gt training of GFL head (#4631)

  • Fix iou_thrs bug in RPN evaluation (#4581)

  • Fix syntax error of config when upgrading model version (#4584)

Improvements

  • Refactor unit test file structures (#4600)

  • Refactor nms config (#4636)

  • Get loading pipeline by checking the class directly rather than through config strings (#4619)

  • Add doctests for mask target generation and mask structures (#4614)

  • Use deep copy when copying pipeline arguments (#4621)

  • Update documentations (#4642, #4650, #4620, #4630)

  • Remove redundant code calling import_modules_from_strings (#4601)

  • Clean deprecated FP16 API (#4571)

  • Check whether CLASSES is correctly initialized in the initialization of XMLDataset (#4555)

  • Support batch inference in the inference API (#4462, #4526)

  • Clean deprecated warning and fix ‘meta’ error (#4695)

v2.9.0 (01/02/2021)

Highlights

  • Support new methods: SCNet, Sparse R-CNN

  • Move train_cfg and test_cfg into model in configs

  • Support to visualize results based on prediction quality

New Features

  • Support SCNet (#4356)

  • Support Sparse R-CNN (#4219)

  • Support evaluate mAP by multiple IoUs (#4398)

  • Support concatenate dataset for testing (#4452)

  • Support to visualize results based on prediction quality (#4441)

  • Add ONNX simplify option to Pytorch2ONNX script (#4468)

  • Add hook for checking compatibility of class numbers in heads and datasets (#4508)

Bug Fixes

  • Fix CPU inference bug of Cascade RPN (#4410)

  • Fix NMS error of CornerNet when there is no prediction box (#4409)

  • Fix TypeError in CornerNet inference (#4411)

  • Fix bug of PAA when training with background images (#4391)

  • Fix the error that the window data is not destroyed when out_file is not None and show==False (#4442)

  • Fix order of NMS score_factor that will decrease the performance of YOLOv3 (#4473)

  • Fix bug in HTC TTA when the number of detection boxes is 0 (#4516)

  • Fix resize error in mask data structures (#4520)

Improvements

  • Allow to customize classes in LVIS dataset (#4382)

  • Add tutorials for building new models with existing datasets (#4396)

  • Add CPU compatibility information in documentation (#4405)

  • Add documentation of deprecated ImageToTensor for batch inference (#4408)

  • Add more details in documentation for customizing dataset (#4430)

  • Switch imshow_det_bboxes visualization backend from OpenCV to Matplotlib (#4389)

  • Deprecate ImageToTensor in image_demo.py (#4400)

  • Move train_cfg/test_cfg into model (#4347, #4489)

  • Update docstring for reg_decoded_bbox option in bbox heads (#4467)

  • Update dataset information in documentation (#4525)

  • Release pre-trained R50 and R101 PAA detectors with multi-scale 3x training schedules (#4495)

  • Add guidance for speed benchmark (#4537)

v2.8.0 (04/01/2021)

Highlights

New Features

Bug Fixes

  • Fix bug of show result in async_benchmark (#4367)

  • Fix scale factor in MaskTestMixin (#4366)

  • Fix but when returning indices in multiclass_nms (#4362)

  • Fix bug of empirical attention in resnext backbone error (#4300)

  • Fix bug of img_norm_cfg in FCOS-HRNet models with updated performance and models (#4250)

  • Fix invalid checkpoint and log in Mask R-CNN models on Cityscapes dataset (#4287)

  • Fix bug in distributed sampler when dataset is too small (#4257)

  • Fix bug of ‘PAFPN has no attribute extra_convs_on_inputs’ (#4235)

Improvements

  • Update model url from aws to aliyun (#4349)

  • Update ATSS for PyTorch 1.6+ (#4359)

  • Update script to install ruby in pre-commit installation (#4360)

  • Delete deprecated mmdet.ops (#4325)

  • Refactor hungarian assigner for more general usage in Sparse R-CNN (#4259)

  • Handle scipy import in DETR to reduce package dependencies (#4339)

  • Update documentation of usages for config options after MMCV (1.2.3) supports overriding list in config (#4326)

  • Update pre-train models of faster rcnn trained on COCO subsets (#4307)

  • Avoid zero or too small value for beta in Dynamic R-CNN (#4303)

  • Add doccumentation for Pytorch2ONNX (#4271)

  • Add deprecated warning FPN arguments (#4264)

  • Support returning indices of kept bboxes when using nms (#4251)

  • Update type and device requirements when creating tensors GFLHead (#4210)

  • Update device requirements when creating tensors in CrossEntropyLoss (#4224)

v2.7.0 (30/11/2020)

  • Support new method: DETR, ResNest, Faster R-CNN DC5.

  • Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX.

New Features

  • Support DETR (#4201, #4206)

  • Support to link the best checkpoint in training (#3773)

  • Support to override config through options in inference.py (#4175)

  • Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX (#4087, #4083)

  • Support ResNeSt backbone (#2959)

  • Support unclip border bbox regression (#4076)

  • Add tpfp func in evaluating AP (#4069)

  • Support mixed precision training of SSD detector with other backbones (#4081)

  • Add Faster R-CNN DC5 models (#4043)

Bug Fixes

  • Fix bug of gpu_id in distributed training mode (#4163)

  • Support Albumentations with version higher than 0.5 (#4032)

  • Fix num_classes bug in faster rcnn config (#4088)

  • Update code in docs/2_new_data_model.md (#4041)

Improvements

  • Ensure DCN offset to have similar type as features in VFNet (#4198)

  • Add config links in README files of models (#4190)

  • Add tutorials for loss conventions (#3818)

  • Add solution to installation issues in 30-series GPUs (#4176)

  • Update docker version in get_started.md (#4145)

  • Add model statistics and polish some titles in configs README (#4140)

  • Clamp neg probability in FreeAnchor (#4082)

  • Speed up expanding large images (#4089)

  • Fix Pytorch 1.7 incompatibility issues (#4103)

  • Update trouble shooting page to resolve segmentation fault (#4055)

  • Update aLRP-Loss in project page (#4078)

  • Clean duplicated reduce_mean function (#4056)

  • Refactor Q&A (#4045)

v2.6.0 (1/11/2020)

  • Support new method: VarifocalNet.

  • Refactored documentation with more tutorials.

New Features

  • Support GIoU calculation in BboxOverlaps2D, and re-implement giou_loss using bbox_overlaps (#3936)

  • Support random sampling in CPU mode (#3948)

  • Support VarifocalNet (#3666, #4024)

Bug Fixes

  • Fix SABL validating bug in Cascade R-CNN (#3913)

  • Avoid division by zero in PAA head when num_pos=0 (#3938)

  • Fix temporary directory bug of multi-node testing error (#4034, #4017)

  • Fix --show-dir option in test script (#4025)

  • Fix GA-RetinaNet r50 model url (#3983)

  • Update code in docs and fix broken urls (#3947)

Improvements

  • Refactor pytorch2onnx API into mmdet.core.export and use generate_inputs_and_wrap_model for pytorch2onnx (#3857, #3912)

  • Update RPN upgrade scripts for v2.5.0 compatibility (#3986)

  • Use mmcv tensor2imgs (#4010)

  • Update test robustness (#4000)

  • Update trouble shooting page (#3994)

  • Accelerate PAA training speed (#3985)

  • Support batch_size > 1 in validation (#3966)

  • Use RoIAlign implemented in MMCV for inference in CPU mode (#3930)

  • Documentation refactoring (#4031)

v2.5.0 (5/10/2020)

Highlights

  • Support new methods: YOLACT, CentripetalNet.

  • Add more documentations for easier and more clear usage.

Backwards Incompatible Changes

FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822) Mixed precision training utils in mmdet.core.fp16 are moved to mmcv.runner, including force_fp32, auto_fp16, wrap_fp16_model, and Fp16OptimizerHook. A deprecation warning will be raised if users attempt to import those methods from mmdet.core.fp16, and will be finally removed in V2.10.0.

[0, N-1] represents foreground classes and N indicates background classes for all models. (#3221) Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus self.background_labels in dense_heads is removed and all heads use self.num_classes to indicate the class index of background labels. This change has no effect on the pre-trained models in the v2.x model zoo, but will affect the training of all models with RPN heads. Two-stage detectors whose RPN head uses softmax will be affected because the order of categories is changed.

Only call get_subset_by_classes when test_mode=True and self.filter_empty_gt=True (#3695) Function get_subset_by_classes in dataset is refactored and only filters out images when test_mode=True and self.filter_empty_gt=True. In the original implementation, get_subset_by_classes is not related to the flag self.filter_empty_gt and will only be called when the classes is set during initialization no matter test_mode is True or False. This brings ambiguous behavior and potential bugs in many cases. After v2.5.0, if filter_empty_gt=False, no matter whether the classes are specified in a dataset, the dataset will use all the images in the annotations. If filter_empty_gt=True and test_mode=True, no matter whether the classes are specified, the dataset will call ``get_subset_by_classes` to check the images and filter out images containing no GT boxes. Therefore, the users should be responsible for the data filtering/cleaning process for the test dataset.

New Features

  • Test time augmentation for single stage detectors (#3844, #3638)

  • Support to show the name of experiments during training (#3764)

  • Add Shear, Rotate, Translate Augmentation (#3656, #3619, #3687)

  • Add image-only transformations including Constrast, Equalize, Color, and Brightness. (#3643)

  • Support YOLACT (#3456)

  • Support CentripetalNet (#3390)

  • Support PyTorch 1.6 in docker (#3905)

Bug Fixes

  • Fix the bug of training ATSS when there is no ground truth boxes (#3702)

  • Fix the bug of using Focal Loss when there is num_pos is 0 (#3702)

  • Fix the label index mapping in dataset browser (#3708)

  • Fix Mask R-CNN training stuck problem when their is no positive rois (#3713)

  • Fix the bug of self.rpn_head.test_cfg in RPNTestMixin by using self.rpn_head in rpn head (#3808)

  • Fix deprecated Conv2d from mmcv.ops (#3791)

  • Fix device bug in RepPoints (#3836)

  • Fix SABL validating bug (#3849)

  • Use https://download.openmmlab.com/mmcv/dist/index.html for installing MMCV (#3840)

  • Fix nonzero in NMS for PyTorch 1.6.0 (#3867)

  • Fix the API change bug of PAA (#3883)

  • Fix typo in bbox_flip (#3886)

  • Fix cv2 import error of ligGL.so.1 in Dockerfile (#3891)

Improvements

  • Change to use mmcv.utils.collect_env for collecting environment information to avoid duplicate codes (#3779)

  • Update checkpoint file names to v2.0 models in documentation (#3795)

  • Update tutorials for changing runtime settings (#3778), modifying loss (#3777)

  • Improve the function of simple_test_bboxes in SABL (#3853)

  • Convert mask to bool before using it as img’s index for robustness and speedup (#3870)

  • Improve documentation of modules and dataset customization (#3821)

v2.4.0 (5/9/2020)

Highlights

  • Fix lots of issues/bugs and reorganize the trouble shooting page

  • Support new methods SABL, YOLOv3, and PAA Assign

  • Support Batch Inference

  • Start to publish mmdet package to PyPI since v2.3.0

  • Switch model zoo to download.openmmlab.com

Backwards Incompatible Changes

  • Support Batch Inference (#3564, #3686, #3705): Since v2.4.0, MMDetection could inference model with multiple images in a single GPU. This change influences all the test APIs in MMDetection and downstream codebases. To help the users migrate their code, we use replace_ImageToTensor (#3686) to convert legacy test data pipelines during dataset initialization.

  • Support RandomFlip with horizontal/vertical/diagonal direction (#3608): Since v2.4.0, MMDetection supports horizontal/vertical/diagonal flip in the data augmentation. This influences bounding box, mask, and image transformations in data augmentation process and the process that will map those data back to the original format.

  • Migrate to use mmlvis and mmpycocotools for COCO and LVIS dataset (#3727). The APIs are fully compatible with the original lvis and pycocotools. Users need to uninstall the existing pycocotools and lvis packages in their environment first and install mmlvis & mmpycocotools.

Bug Fixes

  • Fix default mean/std for onnx (#3491)

  • Fix coco evaluation and add metric items (#3497)

  • Fix typo for install.md (#3516)

  • Fix atss when sampler per gpu is 1 (#3528)

  • Fix import of fuse_conv_bn (#3529)

  • Fix bug of gaussian_target, update unittest of heatmap (#3543)

  • Fixed VOC2012 evaluate (#3553)

  • Fix scale factor bug of rescale (#3566)

  • Fix with_xxx_attributes in base detector (#3567)

  • Fix boxes scaling when number is 0 (#3575)

  • Fix rfp check when neck config is a list (#3591)

  • Fix import of fuse conv bn in benchmark.py (#3606)

  • Fix webcam demo (#3634)

  • Fix typo and itemize issues in tutorial (#3658)

  • Fix error in distributed training when some levels of FPN are not assigned with bounding boxes (#3670)

  • Fix the width and height orders of stride in valid flag generation (#3685)

  • Fix weight initialization bug in Res2Net DCN (#3714)

  • Fix bug in OHEMSampler (#3677)

New Features

  • Support Cutout augmentation (#3521)

  • Support evaluation on multiple datasets through ConcatDataset (#3522)

  • Support PAA assign #(3547)

  • Support eval metric with pickle results (#3607)

  • Support YOLOv3 (#3083)

  • Support SABL (#3603)

  • Support to publish to Pypi in github-action (#3510)

  • Support custom imports (#3641)

Improvements

  • Refactor common issues in documentation (#3530)

  • Add pytorch 1.6 to CI config (#3532)

  • Add config to runner meta (#3534)

  • Add eval-option flag for testing (#3537)

  • Add init_eval to evaluation hook (#3550)

  • Add include_bkg in ClassBalancedDataset (#3577)

  • Using config’s loading in inference_detector (#3611)

  • Add ATSS ResNet-101 models in model zoo (#3639)

  • Update urls to download.openmmlab.com (#3665)

  • Support non-mask training for CocoDataset (#3711)

v2.3.0 (5/8/2020)

Highlights

  • The CUDA/C++ operators have been moved to mmcv.ops. For backward compatibility mmdet.ops is kept as warppers of mmcv.ops.

  • Support new methods CornerNet, DIOU/CIOU loss, and new dataset: LVIS V1

  • Provide more detailed colab training tutorials and more complete documentation.

  • Support to convert RetinaNet from Pytorch to ONNX.

Bug Fixes

  • Fix the model initialization bug of DetectoRS (#3187)

  • Fix the bug of module names in NASFCOSHead (#3205)

  • Fix the filename bug in publish_model.py (#3237)

  • Fix the dimensionality bug when inside_flags.any() is False in dense heads (#3242)

  • Fix the bug of forgetting to pass flip directions in MultiScaleFlipAug (#3262)

  • Fixed the bug caused by default value of stem_channels (#3333)

  • Fix the bug of model checkpoint loading for CPU inference (#3318, #3316)

  • Fix topk bug when box number is smaller than the expected topk number in ATSSAssigner (#3361)

  • Fix the gt priority bug in center_region_assigner.py (#3208)

  • Fix NaN issue of iou calculation in iou_loss.py (#3394)

  • Fix the bug that iou_thrs is not actually used during evaluation in coco.py (#3407)

  • Fix test-time augmentation of RepPoints (#3435)

  • Fix runtimeError caused by incontiguous tensor in Res2Net+DCN (#3412)

New Features

  • Support CornerNet (#3036)

  • Support DIOU/CIOU loss (#3151)

  • Support LVIS V1 dataset (#)

  • Support customized hooks in training (#3395)

  • Support fp16 training of generalized focal loss (#3410)

  • Support to convert RetinaNet from Pytorch to ONNX (#3075)

Improvements

  • Support to process ignore boxes in ATSS assigner (#3082)

  • Allow to crop images without ground truth in RandomCrop (#3153)

  • Enable the the Accuracy module to set threshold (#3155)

  • Refactoring unit tests (#3206)

  • Unify the training settings of to_float32 and norm_cfg in RegNets configs (#3210)

  • Add colab training tutorials for beginners (#3213, #3273)

  • Move CUDA/C++ operators into mmcv.ops and keep mmdet.ops as warppers for backward compatibility (#3232)(#3457)

  • Update installation scripts in documentation (#3290) and dockerfile (#3320)

  • Support to set image resize backend (#3392)

  • Remove git hash in version file (#3466)

  • Check mmcv version to force version compatibility (#3460)

v2.2.0 (1/7/2020)

Highlights

Bug Fixes

  • Fix FreeAnchor when no gt in image (#3176)

  • Clean up deprecated usage of register_module() (#3092, #3161)

  • Fix pretrain bug in NAS FCOS (#3145)

  • Fix num_classes in SSD (#3142)

  • Fix FCOS warmup (#3119)

  • Fix rstrip in tools/publish_model.py

  • Fix flip_ratio default value in RandomFLip pipeline (#3106)

  • Fix cityscapes eval with ms_rcnn (#3112)

  • Fix RPN softmax (#3056)

  • Fix filename of LVIS@v0.5 (#2998)

  • Fix nan loss by filtering out-of-frame gt_bboxes in COCO (#2999)

  • Fix bug in FSAF (#3018)

  • Add FocalLoss num_classes check (#2964)

  • Fix PISA Loss when there are no gts (#2992)

  • Avoid nan in iou_calculator (#2975)

  • Prevent possible bugs in loading and transforms caused by shallow copy (#2967)

New Features

  • Add DetectoRS (#3064)

  • Support Generalize Focal Loss (#3097)

  • Support PointRend (#2752)

  • Support Dynamic R-CNN (#3040)

  • Add DeepFashion dataset (#2968)

  • Implement FCOS training tricks (#2935)

  • Use BaseDenseHead as base class for anchor-base heads (#2963)

  • Add with_cp for BasicBlock (#2891)

  • Add stem_channels argument for ResNet (#2954)

Improvements

  • Add anchor free base head (#2867)

  • Migrate to github action (#3137)

  • Add docstring for datasets, pipelines, core modules and methods (#3130, #3125, #3120)

  • Add VOC benchmark (#3060)

  • Add concat mode in GRoI (#3098)

  • Remove cmd arg autorescale-lr (#3080)

  • Use len(data['img_metas']) to indicate num_samples (#3073, #3053)

  • Switch to EpochBasedRunner (#2976)

v2.1.0 (8/6/2020)

Highlights

Bug Fixes

  • Change the CLI argument --validate to --no-validate to enable validation after training epochs by default. (#2651)

  • Add missing cython to docker file (#2713)

  • Fix bug in nms cpu implementation (#2754)

  • Fix bug when showing mask results (#2763)

  • Fix gcc requirement (#2806)

  • Fix bug in async test (#2820)

  • Fix mask encoding-decoding bugs in test API (#2824)

  • Fix bug in test time augmentation (#2858, #2921, #2944)

  • Fix a typo in comment of apis/train (#2877)

  • Fix the bug of returning None when no gt bboxes are in the original image in RandomCrop. Fix the bug that misses to handle gt_bboxes_ignore, gt_label_ignore, and gt_masks_ignore in RandomCrop, MinIoURandomCrop and Expand modules. (#2810)

  • Fix bug of base_channels of regnet (#2917)

  • Fix the bug of logger when loading pre-trained weights in base detector (#2936)

New Features

  • Add IoU models (#2666)

  • Add colab demo for inference

  • Support class agnostic nms (#2553)

  • Add benchmark gathering scripts for development only (#2676)

  • Add mmdet-based project links (#2736, #2767, #2895)

  • Add config dump in training (#2779)

  • Add ClassBalancedDataset (#2721)

  • Add res2net backbone (#2237)

  • Support RegNetX models (#2710)

  • Use mmcv.FileClient to support different storage backends (#2712)

  • Add ClassBalancedDataset (#2721)

  • Code Release: Prime Sample Attention in Object Detection (CVPR 2020) (#2626)

  • Implement NASFCOS (#2682)

  • Add class weight in CrossEntropyLoss (#2797)

  • Support LVIS dataset (#2088)

  • Support GRoIE (#2584)

Improvements

  • Allow different x and y strides in anchor heads. (#2629)

  • Make FSAF loss more robust to no gt (#2680)

  • Compute pure inference time instead (#2657) and update inference speed (#2730)

  • Avoided the possibility that a patch with 0 area is cropped. (#2704)

  • Add warnings when deprecated imgs_per_gpu is used. (#2700)

  • Add a mask rcnn example for config (#2645)

  • Update model zoo (#2762, #2866, #2876, #2879, #2831)

  • Add ori_filename to img_metas and use it in test show-dir (#2612)

  • Use img_fields to handle multiple images during image transform (#2800)

  • Add upsample_cfg support in FPN (#2787)

  • Add ['img'] as default img_fields for back compatibility (#2809)

  • Rename the pretrained model from open-mmlab://resnet50_caffe and open-mmlab://resnet50_caffe_bgr to open-mmlab://detectron/resnet50_caffe and open-mmlab://detectron2/resnet50_caffe. (#2832)

  • Added sleep(2) in test.py to reduce hanging problem (#2847)

  • Support c10::half in CARAFE (#2890)

  • Improve documentations (#2918, #2714)

  • Use optimizer constructor in mmcv and clean the original implementation in mmdet.core.optimizer (#2947)

v2.0.0 (6/5/2020)

In this release, we made lots of major refactoring and modifications.

  1. Faster speed. We optimize the training and inference speed for common models, achieving up to 30% speedup for training and 25% for inference. Please refer to model zoo for details.

  2. Higher performance. We change some default hyperparameters with no additional cost, which leads to a gain of performance for most models. Please refer to compatibility for details.

  3. More documentation and tutorials. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it here.

  4. Support PyTorch 1.5. The support for 1.1 and 1.2 is dropped, and we switch to some new APIs.

  5. Better configuration system. Inheritance is supported to reduce the redundancy of configs.

  6. Better modular design. Towards the goal of simplicity and flexibility, we simplify some encapsulation while add more other configurable modules like BBoxCoder, IoUCalculator, OptimizerConstructor, RoIHead. Target computation is also included in heads and the call hierarchy is simpler.

  7. Support new methods: FSAF and PAFPN (part of PAFPN).

Breaking Changes Models training with MMDetection 1.x are not fully compatible with 2.0, please refer to the compatibility doc for the details and how to migrate to the new version.

Improvements

  • Unify cuda and cpp API for custom ops. (#2277)

  • New config files with inheritance. (#2216)

  • Encapsulate the second stage into RoI heads. (#1999)

  • Refactor GCNet/EmpericalAttention into plugins. (#2345)

  • Set low quality match as an option in IoU-based bbox assigners. (#2375)

  • Change the codebase’s coordinate system. (#2380)

  • Refactor the category order in heads. 0 means the first positive class instead of background now. (#2374)

  • Add bbox sampler and assigner registry. (#2419)

  • Speed up the inference of RPN. (#2420)

  • Add train_cfg and test_cfg as class members in all anchor heads. (#2422)

  • Merge target computation methods into heads. (#2429)

  • Add bbox coder to support different bbox encoding and losses. (#2480)

  • Unify the API for regression loss. (#2156)

  • Refactor Anchor Generator. (#2474)

  • Make lr an optional argument for optimizers. (#2509)

  • Migrate to modules and methods in MMCV. (#2502, #2511, #2569, #2572)

  • Support PyTorch 1.5. (#2524)

  • Drop the support for Python 3.5 and use F-string in the codebase. (#2531)

Bug Fixes

  • Fix the scale factors for resized images without keep the aspect ratio. (#2039)

  • Check if max_num > 0 before slicing in NMS. (#2486)

  • Fix Deformable RoIPool when there is no instance. (#2490)

  • Fix the default value of assigned labels. (#2536)

  • Fix the evaluation of Cityscapes. (#2578)

New Features

  • Add deep_stem and avg_down option to ResNet, i.e., support ResNetV1d. (#2252)

  • Add L1 loss. (#2376)

  • Support both polygon and bitmap for instance masks. (#2353, #2540)

  • Support CPU mode for inference. (#2385)

  • Add optimizer constructor for complicated configuration of optimizers. (#2397, #2488)

  • Implement PAFPN. (#2392)

  • Support empty tensor input for some modules. (#2280)

  • Support for custom dataset classes without overriding it. (#2408, #2443)

  • Support to train subsets of coco dataset. (#2340)

  • Add iou_calculator to potentially support more IoU calculation methods. (2405)

  • Support class wise mean AP (was removed in the last version). (#2459)

  • Add option to save the testing result images. (#2414)

  • Support MomentumUpdaterHook. (#2571)

  • Add a demo to inference a single image. (#2605)

v1.1.0 (24/2/2020)

Highlights

  • Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts.

  • Support new methods: CARAFE.

Breaking Changes

  • The new MMDDP inherits from the official DDP, thus the __init__ api is changed to be the same as official DDP.

  • The mask_head field in HTC config files is modified.

  • The evaluation and testing script is updated.

  • In all transforms, instance masks are stored as a numpy array shaped (n, h, w) instead of a list of (h, w) arrays, where n is the number of instances.

Bug Fixes

  • Fix IOU assigners when ignore_iof_thr > 0 and there is no pred boxes. (#2135)

  • Fix mAP evaluation when there are no ignored boxes. (#2116)

  • Fix the empty RoI input for Deformable RoI Pooling. (#2099)

  • Fix the dataset settings for multiple workflows. (#2103)

  • Fix the warning related to torch.uint8 in PyTorch 1.4. (#2105)

  • Fix the inference demo on devices other than gpu:0. (#2098)

  • Fix Dockerfile. (#2097)

  • Fix the bug that pad_val is unused in Pad transform. (#2093)

  • Fix the albumentation transform when there is no ground truth bbox. (#2032)

Improvements

  • Use torch instead of numpy for random sampling. (#2094)

  • Migrate to the new MMDDP implementation in MMCV v0.3. (#2090)

  • Add meta information in logs. (#2086)

  • Rewrite Soft NMS with pytorch extension and remove cython as a dependency. (#2056)

  • Rewrite dataset evaluation. (#2042, #2087, #2114, #2128)

  • Use numpy array for masks in transforms. (#2030)

New Features

  • Implement “CARAFE: Content-Aware ReAssembly of FEatures”. (#1583)

  • Add worker_init_fn() in data_loader when seed is set. (#2066, #2111)

  • Add logging utils. (#2035)

v1.0.0 (30/1/2020)

This release mainly improves the code quality and add more docstrings.

Highlights

  • Documentation is online now: https://mmdetection.readthedocs.io.

  • Support new models: ATSS.

  • DCN is now available with the api build_conv_layer and ConvModule like the normal conv layer.

  • A tool to collect environment information is available for trouble shooting.

Bug Fixes

  • Fix the incompatibility of the latest numpy and pycocotools. (#2024)

  • Fix the case when distributed package is unavailable, e.g., on Windows. (#1985)

  • Fix the dimension issue for refine_bboxes(). (#1962)

  • Fix the typo when seg_prefix is a list. (#1906)

  • Add segmentation map cropping to RandomCrop. (#1880)

  • Fix the return value of ga_shape_target_single(). (#1853)

  • Fix the loaded shape of empty proposals. (#1819)

  • Fix the mask data type when using albumentation. (#1818)

Improvements

  • Enhance AssignResult and SamplingResult. (#1995)

  • Add ability to overwrite existing module in Registry. (#1982)

  • Reorganize requirements and make albumentations and imagecorruptions optional. (#1969)

  • Check NaN in SSDHead. (#1935)

  • Encapsulate the DCN in ResNe(X)t into a ConvModule & Conv_layers. (#1894)

  • Refactoring for mAP evaluation and support multiprocessing and logging. (#1889)

  • Init the root logger before constructing Runner to log more information. (#1865)

  • Split SegResizeFlipPadRescale into different existing transforms. (#1852)

  • Move init_dist() to MMCV. (#1851)

  • Documentation and docstring improvements. (#1971, #1938, #1869, #1838)

  • Fix the color of the same class for mask visualization. (#1834)

  • Remove the option keep_all_stages in HTC and Cascade R-CNN. (#1806)

New Features

  • Add two test-time options crop_mask and rle_mask_encode for mask heads. (#2013)

  • Support loading grayscale images as single channel. (#1975)

  • Implement “Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection”. (#1872)

  • Add sphinx generated docs. (#1859, #1864)

  • Add GN support for flops computation. (#1850)

  • Collect env info for trouble shooting. (#1812)

v1.0rc1 (13/12/2019)

The RC1 release mainly focuses on improving the user experience, and fixing bugs.

Highlights

  • Support new models: FoveaBox, RepPoints and FreeAnchor.

  • Add a Dockerfile.

  • Add a jupyter notebook demo and a webcam demo.

  • Setup the code style and CI.

  • Add lots of docstrings and unit tests.

  • Fix lots of bugs.

Breaking Changes

  • There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679)

Bug Fixes

  • Fix a sampling interval bug in Libra R-CNN. (#1800)

  • Fix the learning rate in SSD300 WIDER FACE. (#1781)

  • Fix the scaling issue when keep_ratio=False. (#1730)

  • Fix typos. (#1721, #1492, #1242, #1108, #1107)

  • Fix the shuffle argument in build_dataloader. (#1693)

  • Clip the proposal when computing mask targets. (#1688)

  • Fix the “index out of range” bug for samplers in some corner cases. (#1610, #1404)

  • Fix the NMS issue on devices other than GPU:0. (#1603)

  • Fix SSD Head and GHM Loss on CPU. (#1578)

  • Fix the OOM error when there are too many gt bboxes. (#1575)

  • Fix the wrong keyword argument nms_cfg in HTC. (#1573)

  • Process masks and semantic segmentation in Expand and MinIoUCrop transforms. (#1550, #1361)

  • Fix a scale bug in the Non Local op. (#1528)

  • Fix a bug in transforms when gt_bboxes_ignore is None. (#1498)

  • Fix a bug when img_prefix is None. (#1497)

  • Pass the device argument to grid_anchors and valid_flags. (#1478)

  • Fix the data pipeline for test_robustness. (#1476)

  • Fix the argument type of deformable pooling. (#1390)

  • Fix the coco_eval when there are only two classes. (#1376)

  • Fix a bug in Modulated DeformableConv when deformable_group>1. (#1359)

  • Fix the mask cropping in RandomCrop. (#1333)

  • Fix zero outputs in DeformConv when not running on cuda:0. (#1326)

  • Fix the type issue in Expand. (#1288)

  • Fix the inference API. (#1255)

  • Fix the inplace operation in Expand. (#1249)

  • Fix the from-scratch training config. (#1196)

  • Fix inplace add in RoIExtractor which cause an error in PyTorch 1.2. (#1160)

  • Fix FCOS when input images has no positive sample. (#1136)

  • Fix recursive imports. (#1099)

Improvements

  • Print the config file and mmdet version in the log. (#1721)

  • Lint the code before compiling in travis CI. (#1715)

  • Add a probability argument for the Expand transform. (#1651)

  • Update the PyTorch and CUDA version in the docker file. (#1615)

  • Raise a warning when specifying --validate in non-distributed training. (#1624, #1651)

  • Beautify the mAP printing. (#1614)

  • Add pre-commit hook. (#1536)

  • Add the argument in_channels to backbones. (#1475)

  • Add lots of docstrings and unit tests, thanks to @Erotemic. (#1603, #1517, #1506, #1505, #1491, #1479, #1477, #1475, #1474)

  • Add support for multi-node distributed test when there is no shared storage. (#1399)

  • Optimize Dockerfile to reduce the image size. (#1306)

  • Update new results of HRNet. (#1284, #1182)

  • Add an argument no_norm_on_lateral in FPN. (#1240)

  • Test the compiling in CI. (#1235)

  • Move docs to a separate folder. (#1233)

  • Add a jupyter notebook demo. (#1158)

  • Support different type of dataset for training. (#1133)

  • Use int64_t instead of long in cuda kernels. (#1131)

  • Support unsquare RoIs for bbox and mask heads. (#1128)

  • Manually add type promotion to make compatible to PyTorch 1.2. (#1114)

  • Allowing validation dataset for computing validation loss. (#1093)

  • Use .scalar_type() instead of .type() to suppress some warnings. (#1070)

New Features

  • Add an option --with_ap to compute the AP for each class. (#1549)

  • Implement “FreeAnchor: Learning to Match Anchors for Visual Object Detection”. (#1391)

  • Support Albumentations for augmentations in the data pipeline. (#1354)

  • Implement “FoveaBox: Beyond Anchor-based Object Detector”. (#1339)

  • Support horizontal and vertical flipping. (#1273, #1115)

  • Implement “RepPoints: Point Set Representation for Object Detection”. (#1265)

  • Add test-time augmentation to HTC and Cascade R-CNN. (#1251)

  • Add a COCO result analysis tool. (#1228)

  • Add Dockerfile. (#1168)

  • Add a webcam demo. (#1155, #1150)

  • Add FLOPs counter. (#1127)

  • Allow arbitrary layer order for ConvModule. (#1078)

v1.0rc0 (27/07/2019)

  • Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators!

  • Support two additional datasets: WIDER FACE and Cityscapes.

  • Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters.

  • Speed up multi-gpu testing.

  • Integrate all compiling and installing in a single script.

v0.6.0 (14/04/2019)

  • Up to 30% speedup compared to the model zoo.

  • Support both PyTorch stable and nightly version.

  • Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions.

v0.6rc0(06/02/2019)

  • Migrate to PyTorch 1.0.

v0.5.7 (06/02/2019)

  • Add support for Deformable ConvNet v2. (Many thanks to the authors and @chengdazhi)

  • This is the last release based on PyTorch 0.4.1.

v0.5.6 (17/01/2019)

  • Add support for Group Normalization.

  • Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead.

v0.5.5 (22/12/2018)

  • Add SSD for COCO and PASCAL VOC.

  • Add ResNeXt backbones and detection models.

  • Refactoring for Samplers/Assigners and add OHEM.

  • Add VOC dataset and evaluation scripts.

v0.5.4 (27/11/2018)

  • Add SingleStageDetector and RetinaNet.

v0.5.3 (26/11/2018)

  • Add Cascade R-CNN and Cascade Mask R-CNN.

  • Add support for Soft-NMS in config files.

v0.5.2 (21/10/2018)

  • Add support for custom datasets.

  • Add a script to convert PASCAL VOC annotations to the expected format.

v0.5.1 (20/10/2018)

  • Add BBoxAssigner and BBoxSampler, the train_cfg field in config files are restructured.

  • ConvFCRoIHead / SharedFCRoIHead are renamed to ConvFCBBoxHead / SharedFCBBoxHead for consistency.

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