Unverified Commit 3e902c3a authored by Wenwei Zhang's avatar Wenwei Zhang Committed by GitHub
Browse files

bump to v2.7.0 (#4205)

* bump to v2.7.0

* update dates

* update version requirements

* rephrase

* resolve comments
parent 159d9251
......@@ -42,7 +42,7 @@ This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
v2.6.0 was released in 1/11/2020.
v2.7.0 was released in 30/11/2020.
Please refer to [changelog.md](docs/changelog.md) for details and release history.
A comparison between v1.x and v2.0 codebases can be found in [compatibility.md](docs/compatibility.md).
......
## Changelog
### v2.7.0 (30/11/2020)
- Support new method: [DETR](https://arxiv.org/abs/2005.12872), [ResNest](https://arxiv.org/abs/2004.08955), Faster R-CNN DC5.
- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX.
#### New Features
- Support [DETR](https://arxiv.org/abs/2005.12872) (#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](https://arxiv.org/abs/2004.08955) 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](https://arxiv.org/abs/2008.13367).
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......@@ -11,10 +11,11 @@ The compatible MMDetection and MMCV versions are as below. Please install the co
| MMDetection version | MMCV version |
|:-------------------:|:-------------------:|
| master | mmcv-full>=1.1.5, <=1.3|
| 2.6.0 | mmcv-full>=1.1.5, <=1.3|
| 2.5.0 | mmcv-full>=1.1.5, <=1.3|
| 2.4.0 | mmcv-full>=1.1.1, <=1.3|
| master | mmcv-full>=1.1.5, <1.3|
| 2.7.0 | mmcv-full>=1.1.5, <1.3|
| 2.6.0 | mmcv-full>=1.1.5, <1.3|
| 2.5.0 | mmcv-full>=1.1.5, <1.3|
| 2.4.0 | mmcv-full>=1.1.1, <1.3|
| 2.3.0 | mmcv-full==1.0.5|
| 2.3.0rc0 | mmcv-full>=1.0.2 |
| 2.2.1 | mmcv==0.6.2 |
......
......@@ -412,4 +412,4 @@ data = dict(
- Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when `filter_empty_gt=True` and `test_mode=False`. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when `filter_empty_gt=True` and `test_mode=False`, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves.
- Since the middle format only has box labels and does not contain the class names, when using `CustomDataset`, users cannot filter out the empty GT images through configs but only do this offline.
- The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in v2.6.0 or v2.7.0 (depends on the progress).
- The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in v2.8.0 or v2.9.0 (depends on the progress).
# Copyright (c) Open-MMLab. All rights reserved.
__version__ = '2.6.0'
__version__ = '2.7.0'
short_version = __version__
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