mmdetection

OpenMMLab Detection Toolbox and Benchmark

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mmdetection - MMDetection V2.24.1 Release

Published by ZwwWayne over 2 years ago

What's Changed

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.24.0...v2.24.1

mmdetection - MMDetection V2.24.0 Release

Published by ZwwWayne over 2 years ago

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.

    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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.23.0...v2.24.0

mmdetection - MMDetection V2.23.0 Release

Published by ZwwWayne over 2 years ago

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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.22.0...v2.23.0

mmdetection - MMDetection V2.22.0 Release

Published by ZwwWayne over 2 years ago

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.

Highlights

New Features

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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.21.0...v2.22.0

mmdetection - MMDetection V2.21.0 Release

Published by ZwwWayne over 2 years ago

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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.20.0...v2.21.0

mmdetection - MMDetection V2.20.0 Release

Published by ZwwWayne almost 3 years ago

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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.19.1...v2.20.0

mmdetection - MMDetection V2.0.0 Release

Published by ZwwWayne almost 3 years ago

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v1.2.0...v2.0.0

mmdetection - v1.2.0

Published by ZwwWayne almost 3 years ago

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v1.1.0...v1.2.0

mmdetection - v1.1.0

Published by ZwwWayne almost 3 years ago

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v1.0.0...v1.1.0

mmdetection - v1.0.0

Published by ZwwWayne almost 3 years ago

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v0.6.0...v1.0.0

mmdetection - v2.1.0

Published by ZwwWayne almost 3 years ago

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.0.0...v2.1.0

mmdetection - MMDetection V2.19.1 Release

Published by ZwwWayne almost 3 years ago

mmdetection - MMDetection V2.19.0 Release

Published by ZwwWayne almost 3 years ago

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

New Contributors

mmdetection - MMDetection V2.18.1 Release

Published by ZwwWayne almost 3 years ago

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

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.18.0...v2.18.1

mmdetection - MMDetection V2.18.0 Release

Published by ZwwWayne almost 3 years ago

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 bug (#6293)
  • Fix initialization 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 detector (#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 typos (#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 heads and speed-up (#6268)
  • Migrate to use prior_generator in the 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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.17.0...v2.18.0

mmdetection - MMDetection V2.17.0 Release

Published by ZwwWayne about 3 years ago

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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.16.0...v2.17.0

mmdetection - MMDetection V2.16.0 Release

Published by ZwwWayne about 3 years ago

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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.15.1...v2.16.0

mmdetection - MMDetection V2.15.1 Release

Published by ZwwWayne about 3 years ago

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

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.15.0...v2.15.1

mmdetection - MMDetection V2.15.0 Release

Published by ZwwWayne about 3 years ago

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

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.14.0...v2.15.0

mmdetection - MMDetection V2.14.0 Release

Published by ZwwWayne over 3 years ago

Highlights

  • Upgrade to use MMCV-full 1.3.8 to use the fix in https://github.com/open-mmlab/mmcv/pull/1120
  • 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)

New Contributors

Full Changelog: https://github.com/open-mmlab/mmdetection/compare/v2.13.0...v2.14.0

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