Bump version to v2.22.0 (#7249)

* [Enhancement] Upgrade isort in pre-commit hook (#7130) * upgrade isort to v5.10.1 * replace known_standard_library with extra_standard_library * upgrade isort to v5.10.1 replace known_standard_library with extra_standard_library * imports order changes * [Fix] cannot to save the best checkpoint when the key_score is None (#7101) * [Fix] Fix MixUp transform filter boxes failing case. Added test case (#7080) * [Fix] Update the version limitation of mmcv-full and pytorch in CI. (#7133) * Update * Update build.yml * Update build.yml * [Feature] Support TIMMBackbone (#7020) * add TIMMBackbone based on open-mmlab/mmpretrain#427 open-mmlab/mmsegmentation#998 * update and clean * fix unit test * Revert * add example configs * Create 2_new_data_model.md (#6476) fix some typo Co-authored-by: PJLAB\huanghaian <[email protected]> * [FIX] add Ci of pytorch 1.10 and comments for bbox clamp (#7081) (#7083) * add comments for bbox clamp * add CI of pytorch1.10 * add ci of pytorch1.10.1 * mmcv1.9.0->mmcv1.9 * add ci of pytorch1.10 * Add daily issue owners (#7163) * Add code owners Signed-off-by: del-zhenwu <[email protected]> * Update code owners Signed-off-by: del-zhenwu <[email protected]> * [Feature] Support visualization for Panoptic Segmentation (#7041) * First commit of v2 * split the functions * Support to show panoptic result * temp * Support to show gt * support show gt * fix lint * Support to browse datasets * Fix unit tests * Fix findContours * fix comments * Fix pre-commit * fix lint * Add the type of an argument * [Fix] confusion_matrix.py analysis tool handling NaNs (#7147) * [Fix] Added missing property in SABLHead (#7091) * Added missing property in SABLHead * set pre-commit-hooks to v0.1.0 * set maskdownlint to v0.11.0 * pre-commit-hooks Co-authored-by: Cedric Luo <[email protected]> * Update config.md (#7215) * [Fix] Fix wrong img name in onnx2tensorrt.py (#7157) * [Docs] fix albumentations installed way (#7143) * Update config.md fix some typos Co-authored-by: Jamie <[email protected]> Co-authored-by: BigDong <[email protected]> * [Feature] Support DyHead (#6823) * add DyHead * move and update DYReLU * update * replace stack with sum to reduce memory * clean and update * update to align inference accuracy (incomplete) * fix pad * update to align training accuracy and pick #6867 * add README and metafile * update docs * resolve comments * revert picking 6867 * update README.md * update metafile.yml * resolve comments and update urls * Fix broken colab link (#7218) * [Fix] Fix wrong img name in onnx2tensorrt.py (#7157) * [Docs] fix albumentations installed way (#7143) * Fix broken colab link Co-authored-by: Jamie <[email protected]> Co-authored-by: BigDong <[email protected]> * Remove the inplace addition in `FPN` (#7175) * [Fix] Fix wrong img name in onnx2tensorrt.py (#7157) * [Docs] fix albumentations installed way (#7143) * Remove the inplace addition in `FPN` * update Co-authored-by: Jamie <[email protected]> Co-authored-by: BigDong <[email protected]> Co-authored-by: PJLAB\huanghaian <[email protected]> * [Feature] Support OpenImages Dataset (#6331) * [Feature] support openimage group of eval * [Feature] support openimage group of eval * support openimage dataset * support openimage challenge dataset * fully support OpenImages-V6 and OpenImages Challenge 2019 * Fix some logic error * update config file * fix get data_infos error * fully support OpenImages evaluation * update OpenImages config files * [Feature] support OpenImages datasets * fix bug * support load image metas from pipeline * fix bug * fix get classes logic error * update code * support get image metas * support openimags * support collect image metas * support Open Images * fix openimages logic * minor fix * add a new function to compute openimages tpfp * minor fix * fix ci error * minor fix * fix indication * minor fix * fix returns * fix returns * fix returns * fix returns * fix returns * minor fix * update readme * support loading image level labels and fix some logic * minor fix * minor fix * add class names * minor fix * minor fix * minor fix * add openimages test unit * minor fix * minor fix * fix test unit * minor fix * fix logic error * minor fix * fully support openimages * minor fix * fix docstring * fix docstrings in readthedocs * update get image metas script * label_description_file -> label_file * update openimages readme * fix test unit * fix test unit * minor fix * update readme file * Update get_image_metas.py * [Enhance] Speed up SimOTA matching. (#7098) * [Feature] Add Maskformer to mmdet (#7212) * first commit * add README * move model description from config to readme add description for binary_input add description for dice loss add a independent panoptic gt processing function add a independent panoptic gt processing function remove compatibility of pretrain in maskformer * update comments in maskformer_head * update docs format * Add deprecation message for deploy tools (#7242) * Add CI for windows (#7228) * [Fix] Fix wrong img name in onnx2tensorrt.py (#7157) * [Docs] fix albumentations installed way (#7143) * Add mmrotate (#7216) * fix description for args in CSPDarknet (#7187) * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * Update build.yml * fix test_find_latest_checkpoint * fix data_infos__default_db_directories * fix test_custom_classes_override_default * Update test_custom_dataset.py * Update test_common.py * Update assign_result.py * use os.path.join * fix bug * Update test_common.py * Update assign_result.py * Update sampling_result.py * os.path -> osp * os.path -> osp Co-authored-by: Jamie <[email protected]> Co-authored-by: BigDong <[email protected]> Co-authored-by: Hyeokjoon Kwon <[email protected]> * add Chinese version of init_cfg (#7188) * [Fix] Fix wrong img name in onnx2tensorrt.py (#7157) * [Docs] fix albumentations installed way (#7143) * Create init_cfg.md * Update docs/zh_cn/tutorials/init_cfg.md Co-authored-by: Haian Huang(深度眸) <[email protected]> * update init_cfg.md * update init_cfg.md * update init_cfg.md * update init_cfg.md Co-authored-by: Jamie <[email protected]> Co-authored-by: BigDong <[email protected]> Co-authored-by: Haian Huang(深度眸) <[email protected]> * update MaskFormer readme and docs (#7241) * update docs for maskformer * update readme * update readme format * update link * update json link * update format of ConfigDict * update format of function returns * uncomment main in deployment/test.py * [Feature] ResNet Strikes Back. (#7001) * [Feature] ResNet Strikes Back. * add more cfg * add readme * update * update * update * update * update * update * Maskformer Visualization (#7247) * maskformer visualization * compatible with models that do not contain arg of pretrained * compatible with models that do not contain arg of pretrained * Bump versions to v2.22.0 (#7240) * Bump versions to v2.22.0 * Fix comments and add the latest PRs * fix the id of contributor * relax the version of mmcv * Add ResNet Strikes Back * Update README_zh-CN.md * Update README.md * fix typo * Update README_zh-CN.md Co-authored-by: Wenwei Zhang <[email protected]> * Maskformer metafile and rsb readme format (#7250) * [Fix] Fix Open Images testunit to avoid error in Windows CI (#7252) * [Feature] support openimage group of eval * [Feature] support openimage group of eval * support openimage dataset * support openimage challenge dataset * fully support OpenImages-V6 and OpenImages Challenge 2019 * Fix some logic error * update config file * fix get data_infos error * fully support OpenImages evaluation * update OpenImages config files * [Feature] support OpenImages datasets * fix bug * support load image metas from pipeline * fix bug * fix get classes logic error * update code * support get image metas * support openimags * support collect image metas * support Open Images * fix openimages logic * minor fix * add a new function to compute openimages tpfp * minor fix * fix ci error * minor fix * fix indication * minor fix * fix returns * fix returns * fix returns * fix returns * fix returns * minor fix * update readme * support loading image level labels and fix some logic * minor fix * minor fix * add class names * minor fix * minor fix * minor fix * add openimages test unit * minor fix * minor fix * fix test unit * minor fix * fix logic error * minor fix * fully support openimages * minor fix * fix docstring * fix docstrings in readthedocs * update get image metas script * label_description_file -> label_file * update openimages readme * fix test unit * fix test unit * minor fix * update readme file * Update get_image_metas.py * fix oid testunit to avoid some error in windows * minor fix to avoid some error in windows * minor fix * add comments in oid test unit * minor fix Co-authored-by: Cedric Luo <[email protected]> Co-authored-by: LuooChen <[email protected]> Co-authored-by: Daniel van Sabben Alsina <[email protected]> Co-authored-by: jbwang1997 <[email protected]> Co-authored-by: Yosuke Shinya <[email protected]> Co-authored-by: siatwangmin <[email protected]> Co-authored-by: PJLAB\huanghaian <[email protected]> Co-authored-by: del-zhenwu <[email protected]> Co-authored-by: Guangchen Lin <[email protected]> Co-authored-by: VIKASH RANJAN <[email protected]> Co-authored-by: Range King <[email protected]> Co-authored-by: Jamie <[email protected]> Co-authored-by: BigDong <[email protected]> Co-authored-by: Haofan Wang <[email protected]> Co-authored-by: Zhijian Liu <[email protected]> Co-authored-by: BigDong <[email protected]> Co-authored-by: RangiLyu <[email protected]> Co-authored-by: Yue Zhou <[email protected]> Co-authored-by: Hyeokjoon Kwon <[email protected]> Co-authored-by: Kevin Ye <[email protected]>
open-mmlab · Feb 26, 2022 · e045607 · e045607
1 parent e359d3f
commit e045607
Show file tree

Hide file tree

Showing 124 changed files with 6,706 additions and 369 deletions.
diff --git a/.dev_scripts/benchmark_inference_fps.py b/.dev_scripts/benchmark_inference_fps.py
@@ -7,6 +7,7 @@
 from mmcv import Config, DictAction
 from mmcv.runner import init_dist
 from terminaltables import GithubFlavoredMarkdownTable
+
 from tools.analysis_tools.benchmark import repeat_measure_inference_speed
 
 

diff --git a/.dev_scripts/gather_models.py b/.dev_scripts/gather_models.py
@@ -98,7 +98,9 @@ def get_dataset_name(config):
         LVISV05Dataset='LVIS v0.5',
         LVISV1Dataset='LVIS v1',
         VOCDataset='Pascal VOC',
-        WIDERFaceDataset='WIDER Face')
+        WIDERFaceDataset='WIDER Face',
+        OpenImagesDataset='OpenImagesDataset',
+        OpenImagesChallengeDataset='OpenImagesChallengeDataset')
     cfg = mmcv.Config.fromfile('./configs/' + config)
     return name_map[cfg.dataset_type]
 

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
@@ -33,23 +33,26 @@ jobs:
     strategy:
       matrix:
         python-version: [3.7]
-        torch: [1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0]
+        torch: [1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0, 1.10.1]
         include:
           - torch: 1.5.1
             torchvision: 0.6.1
-            mmcv: 1.5.0
+            mmcv: 1.5
           - torch: 1.6.0
             torchvision: 0.7.0
-            mmcv: 1.6.0
+            mmcv: 1.6
           - torch: 1.7.0
             torchvision: 0.8.1
-            mmcv: 1.7.0
+            mmcv: 1.7
           - torch: 1.8.0
             torchvision: 0.9.0
-            mmcv: 1.8.0
+            mmcv: 1.8
           - torch: 1.9.0
             torchvision: 0.10.0
-            mmcv: 1.9.0
+            mmcv: 1.9
+          - torch: 1.10.1
+            torchvision: 0.11.2
+            mmcv: 1.10
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}
@@ -91,19 +94,19 @@ jobs:
           - torch: 1.5.1+cu101
             torch_version: torch1.5.1
             torchvision: 0.6.1+cu101
-            mmcv: 1.5.0
+            mmcv: 1.5
           - torch: 1.6.0+cu101
             torch_version: torch1.6.0
             torchvision: 0.7.0+cu101
-            mmcv: 1.6.0
+            mmcv: 1.6
           - torch: 1.7.0+cu101
             torch_version: torch1.7.0
             torchvision: 0.8.1+cu101
-            mmcv: 1.7.0
+            mmcv: 1.7
           - torch: 1.8.0+cu101
             torch_version: torch1.8.0
             torchvision: 0.9.0+cu101
-            mmcv: 1.8.0
+            mmcv: 1.8
 
     steps:
       - uses: actions/checkout@v2
@@ -160,12 +163,16 @@ jobs:
     strategy:
       matrix:
         python-version: [3.6, 3.7, 3.8, 3.9]
-        torch: [1.9.0+cu102]
+        torch: [1.9.0+cu102, 1.10.1+cu102]
         include:
           - torch: 1.9.0+cu102
             torch_version: torch1.9.0
             torchvision: 0.10.0+cu102
-            mmcv: 1.9.0
+            mmcv: 1.9
+          - torch: 1.10.1+cu102
+            torch_version: torch1.10.1
+            torchvision: 0.11.2+cu102
+            mmcv: 1.10
 
     steps:
       - uses: actions/checkout@v2
@@ -224,3 +231,50 @@ jobs:
           env_vars: OS,PYTHON
           name: codecov-umbrella
           fail_ci_if_error: false
+
+  build_windows:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2022]
+        python: [3.8]
+        platform: [cpu, cu111]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade --user
+      - name: Install PyTorch
+        # As a complement to Linux CI, we test on PyTorch LTS version
+        run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install MMCV
+        run: pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full
+      - name: Install unittest dependencies
+        run: |
+          python -V
+          python -m pip install pycocotools
+          python -m pip install -r requirements/tests.txt -r requirements/optional.txt
+          python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+          python -m pip install git+https://github.com/cocodataset/panopticapi.git
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Show pip list
+        run: pip list
+      - name: Build and install
+        run: pip install -e .
+      - name: Run unittests
+        run: coverage run --branch --source mmdet -m pytest tests -sv
+      - name: Generate coverage report
+        run: |
+          coverage xml
+          coverage report -m
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v2
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
diff --git a/.owners.yml b/.owners.yml
@@ -0,0 +1,14 @@
+assign:
+  strategy:
+    # random
+    daily-shift-based
+  scedule:
+    '*/1 * * * *'
+  assignees:
+    - Czm369
+    - hhaAndroid
+    - jbwang1997
+    - RangiLyu
+    - BIGWangYuDong
+    - chhluo
+    - ZwwWayne
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -3,12 +3,8 @@ repos:
     rev: 3.8.3
     hooks:
       - id: flake8
-  - repo: https://github.com/asottile/seed-isort-config
-    rev: v2.2.0
-    hooks:
-      - id: seed-isort-config
-  - repo: https://github.com/timothycrosley/isort
-    rev: 4.3.21
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.10.1
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf

diff --git a/README.md b/README.md
@@ -74,10 +74,11 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-**2.21.0** was released in 8/2/2022:
+**2.22.0** was released in 24/2/2022:
 
-- Support CPU training
-- Allow to set parameters about multi-processing to speed up training and testing
+- Support [MaskFormer](configs/maskformer), [DyHead](configs/dyhead), [OpenImages Dataset](configs/openimages) and [TIMM backbone](configs/timm_example)
+- Support visualization for Panoptic Segmentation
+- Release a good recipe of using ResNet in object detectors pre-trained by [ResNet Strikes Back](https://arxiv.org/abs/2110.00476), which consistently brings about 3~4 mAP improvements over RetinaNet, Faster/Mask/Cascade Mask R-CNN
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
@@ -162,6 +163,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
       <td>
         <ul>
           <li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
+          <li><a href="configs/maskformer">MaskFormer (NeurIPS'2019)</a></li>
         </ul>
       </td>
       <td>
@@ -225,6 +227,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         <li><a href="configs/pvt">PVT (ICCV'2021)</a></li>
         <li><a href="configs/swin">Swin (CVPR'2021)</a></li>
         <li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
+        <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
       </ul>
       </td>
       <td>
@@ -234,6 +237,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         <li><a href="configs/carafe">CARAFE (ICCV'2019)</a></li>
         <li><a href="configs/fpg">FPG (ArXiv'2020)</a></li>
         <li><a href="configs/groie">GRoIE (ICPR'2020)</a></li>
+        <li><a href="configs/dyhead">DyHead (CVPR'2021)</a></li>
       </ul>
       </td>
       <td>
@@ -252,6 +256,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
           <li><a href="configs/gn+ws">Weight Standardization (ArXiv'2019)</a></li>
           <li><a href="configs/pisa">Prime Sample Attention (CVPR'2020)</a></li>
           <li><a href="configs/strong_baselines">Strong Baselines (CVPR'2021)</a></li>
+          <li><a href="configs/resnet_strikes_back">Resnet strikes back (ArXiv'2021)</a></li>
         </ul>
       </td>
     </tr>
@@ -321,4 +326,4 @@ If you use this toolbox or benchmark in your research, please cite this project.
 - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
 - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
 - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
-- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -73,10 +73,11 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
 ## 更新日志
 
-最新的 **2.21.0** 版本已经在 2022.02.08 发布:
+最新的 **2.22.0** 版本已经在 2022.02.24 发布:
 
-- 支持了 CPU 训练
-- 允许设置多进程相关的参数来加速训练与推理
+- 支持 [MaskFormer](configs/maskformer)，[DyHead](configs/dyhead)，[OpenImages Dataset](configs/openimages) 和 [TIMM backbone](configs/timm_example)
+- 支持全景分割可视化
+- 发布了一个在目标检测任务中使用 ResNet 的好方法，它是由 [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) 预训练的，并且能稳定的在 RetinaNet, Faster/Mask/Cascade Mask R-CNN 上带来约 3-4 mAP 的提升
 
 如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/changelog.md)。
 
@@ -224,6 +225,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
         <li><a href="configs/pvt">PVT (ICCV'2021)</a></li>
         <li><a href="configs/swin">Swin (CVPR'2021)</a></li>
         <li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
+        <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
       </ul>
       </td>
       <td>
@@ -233,6 +235,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
         <li><a href="configs/carafe">CARAFE (ICCV'2019)</a></li>
         <li><a href="configs/fpg">FPG (ArXiv'2020)</a></li>
         <li><a href="configs/groie">GRoIE (ICPR'2020)</a></li>
+        <li><a href="configs/dyhead">DyHead (CVPR'2021)</a></li>
       </ul>
       </td>
       <td>
@@ -251,6 +254,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
           <li><a href="configs/gn+ws">Weight Standardization (ArXiv'2019)</a></li>
           <li><a href="configs/pisa">Prime Sample Attention (CVPR'2020)</a></li>
           <li><a href="configs/strong_baselines">Strong Baselines (CVPR'2021)</a></li>
+          <li><a href="configs/resnet_strikes_back">Resnet strikes back (ArXiv'2021)</a></li>
         </ul>
       </td>
     </tr>

diff --git a/configs/_base_/datasets/openimages_detection.py b/configs/_base_/datasets/openimages_detection.py
@@ -0,0 +1,65 @@
+# dataset settings
+dataset_type = 'OpenImagesDataset'
+data_root = 'data/OpenImages/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, denorm_bbox=True),
+    dict(type='Resize', img_scale=(1024, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1024, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ],
+    ),
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=0,  # workers_per_gpu > 0 may occur out of memory
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/oidv6-train-annotations-bbox.csv',
+        img_prefix=data_root + 'OpenImages/train/',
+        label_file=data_root + 'annotations/class-descriptions-boxable.csv',
+        hierarchy_file=data_root +
+        'annotations/bbox_labels_600_hierarchy.json',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
+        img_prefix=data_root + 'OpenImages/validation/',
+        label_file=data_root + 'annotations/class-descriptions-boxable.csv',
+        hierarchy_file=data_root +
+        'annotations/bbox_labels_600_hierarchy.json',
+        meta_file=data_root + 'annotations/validation-image-metas.pkl',
+        image_level_ann_file=data_root +
+        'annotations/validation-annotations-human-imagelabels-boxable.csv',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
+        img_prefix=data_root + 'OpenImages/validation/',
+        label_file=data_root + 'annotations/class-descriptions-boxable.csv',
+        hierarchy_file=data_root +
+        'annotations/bbox_labels_600_hierarchy.json',
+        meta_file=data_root + 'annotations/validation-image-metas.pkl',
+        image_level_ann_file=data_root +
+        'annotations/validation-annotations-human-imagelabels-boxable.csv',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='mAP')
diff --git a/configs/dyhead/README.md b/configs/dyhead/README.md
@@ -0,0 +1,46 @@
+# DyHead
+
+> [Dynamic Head: Unifying Object Detection Heads with Attentions](https://arxiv.org/abs/2106.08322)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the performance in various object detection heads but failed to present a unified view. In this paper, we present a novel dynamic head framework to unify object detection heads with attentions. By coherently combining multiple self-attention mechanisms between feature levels for scale-awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness, the proposed approach significantly improves the representation ability of object detection heads without any computational overhead. Further experiments demonstrate that the effectiveness and efficiency of the proposed dynamic head on the COCO benchmark. With a standard ResNeXt-101-DCN backbone, we largely improve the performance over popular object detectors and achieve a new state-of-the-art at 54.0 AP. Furthermore, with latest transformer backbone and extra data, we can push current best COCO result to a new record at 60.6 AP.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/42844407/149169448-fcafb6d0-b866-41cc-9422-94de9f1e1761.png" height="300"/>
+</div>
+
+## Results and Models
+
+| Method | Backbone | Style   | Setting      | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+|:------:|:--------:|:-------:|:------------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
+| ATSS   | R-50     | caffe   | reproduction | 1x      | 5.4      | 13.2           | 42.5   | [config](./atss_r50_caffe_fpn_dyhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939-162888e6.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939.log.json) |
+| ATSS   | R-50     | pytorch | simple       | 1x      | 4.9      | 13.7           | 43.3   | [config](./atss_r50_fpn_dyhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314.log.json) |
+
+- We trained the above models with 4 GPUs and 4 `samples_per_gpu`.
+- The `reproduction` setting aims to reproduce the official implementation based on Detectron2.
+- The `simple` setting serves as a minimum example to use DyHead in MMDetection. Specifically,
+  - it adds `DyHead` to `neck` after `FPN`
+  - it sets `stacked_convs=0` to `bbox_head`
+- The `simple` setting achieves higher AP than the original implementation.
+  We have not conduct ablation study between the two settings.
+  `dict(type='Pad', size_divisor=128)` may further improve AP by prefer spatial alignment across pyramid levels, although large padding reduces efficiency.
+
+## Relation to Other Methods
+
+- DyHead can be regarded as an improved [SEPC](https://arxiv.org/abs/2005.03101) with [DyReLU modules](https://arxiv.org/abs/2003.10027) and simplified [SE blocks](https://arxiv.org/abs/1709.01507).
+- Xiyang Dai et al., the author team of DyHead, adopt it for [Dynamic DETR](https://openaccess.thecvf.com/content/ICCV2021/html/Dai_Dynamic_DETR_End-to-End_Object_Detection_With_Dynamic_Attention_ICCV_2021_paper.html).
+  The description of Dynamic Encoder in Sec. 3.2 will help you understand DyHead.
+
+## Citation
+
+```latex
+@inproceedings{DyHead_CVPR2021,
+  author    = {Dai, Xiyang and Chen, Yinpeng and Xiao, Bin and Chen, Dongdong and Liu, Mengchen and Yuan, Lu and Zhang, Lei},
+  title     = {Dynamic Head: Unifying Object Detection Heads With Attentions},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2021}
+}
+```