- Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts.
- Support new methods: CARAFE.
- The new MMDDP inherits from the official DDP, thus the
__init__api is changed to be the same as official DDP.
mask_headfield 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.
- 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.uint8in PyTorch 1.4. (#2105)
- Fix the inference demo on devices other than gpu:0. (#2098)
- Fix Dockerfile. (#2097)
- Fix the bug that
pad_valis unused in Pad transform. (#2093)
- Fix the albumentation transform when there is no ground truth bbox. (#2032)
- 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)
- Implement “CARAFE: Content-Aware ReAssembly of FEatures”. (#1583)
worker_init_fn()in data_loader when seed is set. (#2066, #2111)
- Add logging utils. (#2035)
This release mainly improves the code quality and add more docstrings.
- Documentation is online now: https://mmdetection.readthedocs.io.
- Support new models: ATSS.
- DCN is now available with the api
ConvModulelike the normal conv layer.
- A tool to collect environment information is available for trouble shooting.
- 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
- Fix the typo when
seg_prefixis a list. (#1906)
- Add segmentation map cropping to RandomCrop. (#1880)
- Fix the return value of
- Fix the loaded shape of empty proposals. (#1819)
- Fix the mask data type when using albumentation. (#1818)
- 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
- 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)
SegResizeFlipPadRescaleinto different existing transforms. (#1852)
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_stagesin HTC and Cascade R-CNN. (#1806)
- Add two test-time options
rle_mask_encodefor 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)
The RC1 release mainly focuses on improving the user experience, and fixing bugs.
- 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.
- There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679)
- Fix a sampling interval bug in Libra R-CNN. (#1800)
- Fix the learning rate in SSD300 WIDER FACE. (#1781)
- Fix the scaling issue when
- Fix typos. (#1721, #1492, #1242, #1108, #1107)
- Fix the shuffle argument in
- 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_cfgin 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_ignoreis None. (#1498)
- Fix a bug when
img_prefixis None. (#1497)
- Pass the device argument to
- 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)
- 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
- Update the PyTorch and CUDA version in the docker file. (#1615)
- Raise a warning when specifying
--validatein non-distributed training. (#1624, #1651)
- Beautify the mAP printing. (#1614)
- Add pre-commit hook. (#1536)
- Add the argument
in_channelsto 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_lateralin 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)
.type()to suppress some warnings. (#1070)
- Add an option
--with_apto 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)
- 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.
- Up to 30% speedup compared to the model zoo.
- Support both PyTorch stable and nightly version.
- Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions.
- Migrate to PyTorch 1.0.
- Add support for Deformable ConvNet v2. (Many thanks to the authors and @chengdazhi)
- This is the last release based on PyTorch 0.4.1.
- Add support for Group Normalization.
- Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead.
- 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.
- Add SingleStageDetector and RetinaNet.
- Add Cascade R-CNN and Cascade Mask R-CNN.
- Add support for Soft-NMS in config files.
- Add support for custom datasets.
- Add a script to convert PASCAL VOC annotations to the expected format.
- Add BBoxAssigner and BBoxSampler, the
train_cfgfield in config files are restructured.
SharedFCRoIHeadare renamed to