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Changelog

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.