添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py

+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
+model = dict(bbox_head=dict(transform_method='partial_minmax'))

openmmlab_test/mmdetection-speed_xinpian/configs/res2net/README.md

+# Res2Net for object detection and instance segmentation
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
+
+|    Backbone     |Params. | GFLOPs  | top-1 err. | top-5 err. |
+| :-------------: |:----:  | :-----: | :--------: | :--------: |
+| ResNet-101      |44.6 M  | 7.8     |  22.63     |  6.44      |
+| ResNeXt-101-64x4d |83.5M | 15.5    |  20.40     |  -         |
+| HRNetV2p-W48    | 77.5M  | 16.1    |  20.70     |  5.50      |
+| Res2Net-101     | 45.2M  | 8.3     |  18.77     |  4.64      |
+
+Compared with other backbone networks, Res2Net requires fewer parameters and FLOPs.
+
+**Note:**
+
+- GFLOPs for classification are calculated with image size (224x224).
+
+```latex
+@article{gao2019res2net,
+  title={Res2Net: A New Multi-scale Backbone Architecture},
+  author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
+  journal={IEEE TPAMI},
+  year={2020},
+  doi={10.1109/TPAMI.2019.2938758},
+}
+```
+
+## Results and Models
+
+### Faster R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
+|R2-101-FPN       | pytorch |   2x   |   7.4    |   -           |  43.0  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco_20200514_231734.log.json) |
+
+### Mask R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
+|R2-101-FPN       | pytorch |    2x   |   7.9    |      -         |   43.6 | 38.7  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco_20200515_002413.log.json) |
+
+### Cascade R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
+|R2-101-FPN       | pytorch |   20e   |   7.8    |      -         |  45.7  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco_20200515_091644.log.json) |
+
+### Cascade Mask R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
+R2-101-FPN       | pytorch |  20e   |    9.5  |      -         |  46.4  |  40.0  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) |
+
+### Hybrid Task Cascade (HTC)
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
+| R2-101-FPN     | pytorch |   20e   |    -    |      -         |  47.5  | 41.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/htc_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco_20200515_150029.log.json) |
+
+- Res2Net ImageNet pretrained models are in [Res2Net-PretrainedModels](https://github.com/Res2Net/Res2Net-PretrainedModels).
+- More applications of Res2Net are in [Res2Net-Github](https://github.com/Res2Net/).

openmmlab_test/mmdetection-speed_xinpian/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py

+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    pretrained='open-mmlab://res2net101_v1d_26w_4s',
+    backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))

openmmlab_test/mmdetection-speed_xinpian/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py

+_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    pretrained='open-mmlab://res2net101_v1d_26w_4s',
+    backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))

openmmlab_test/mmdetection-speed_xinpian/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    pretrained='open-mmlab://res2net101_v1d_26w_4s',
+    backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))

openmmlab_test/mmdetection-speed_xinpian/configs/res2net/htc_r2_101_fpn_20e_coco.py

+_base_ = '../htc/htc_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://res2net101_v1d_26w_4s',
+    backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)

openmmlab_test/mmdetection-speed_xinpian/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    pretrained='open-mmlab://res2net101_v1d_26w_4s',
+    backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26))

openmmlab_test/mmdetection-speed_xinpian/configs/res2net/metafile.yml

+Collections:
+  - Name: Res2Net
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - Res2Net
+    Paper: https://arxiv.org/abs/1904.01169
+    README: configs/res2net/README.md
+
+Models:
+  - Name: faster_rcnn_r2_101_fpn_2x_coco
+    In Collection: Res2Net
+    Config: configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.4
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth
+
+  - Name: mask_rcnn_r2_101_fpn_2x_coco
+    In Collection: Res2Net
+    Config: configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth
+
+  - Name: cascade_rcnn_r2_101_fpn_20e_coco
+    In Collection: Res2Net
+    Config: configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth
+
+  - Name: cascade_mask_rcnn_r2_101_fpn_20e_coco
+    In Collection: Res2Net
+    Config: configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 9.5
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth
+
+  - Name: htc_r2_101_fpn_20e_coco
+    In Collection: Res2Net
+    Config: configs/res2net/htc_r2_101_fpn_20e_coco.py
+    Metadata:
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/README.md

+# ResNeSt: Split-Attention Networks
+
+## Introduction
+
+[BACKBONE]
+
+```latex
+@article{zhang2020resnest,
+title={ResNeSt: Split-Attention Networks},
+author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
+journal={arXiv preprint arXiv:2004.08955},
+year={2020}
+}
+```
+
+## Results and Models
+
+### Faster R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
+|S-50-FPN       | pytorch |   1x   |   4.8  |   -           | 42.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20200926_125502.log.json) |
+|S-101-FPN       | pytorch |   1x   |   7.1  |   -           | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201006_021058.log.json) |
+
+### Mask R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
+|S-50-FPN       | pytorch |    1x   |   5.5  |      -         | 42.6 | 38.1 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20200926_125503.log.json) |
+|S-101-FPN       | pytorch |    1x   |   7.8  |      -         | 45.2 | 40.2 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_215831.log.json) |
+
+### Cascade R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
+|S-50-FPN       | pytorch |   1x   |   -    |   -           |  44.5  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201005_113242.log.json) |
+|S-101-FPN       | pytorch |   1x   |   8.4  |   -           |  46.8  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201122_213640.log.json) |
+
+### Cascade Mask R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
+|S-50-FPN       | pytorch |    1x   |   -    |      -         | 45.4 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201122_104428.log.json) |
+|S-101-FPN       | pytorch |    1x   |  10.5  |      -         | 47.7 | 41.4 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_113243.log.json) |

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py

+_base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://resnest101',
+    backbone=dict(stem_channels=128, depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py

+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    pretrained='open-mmlab://resnest50',
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch'),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_head=dict(norm_cfg=norm_cfg)))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        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', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 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(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py

+_base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://resnest101',
+    backbone=dict(stem_channels=128, depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py

+_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    pretrained='open-mmlab://resnest50',
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch'),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ], ))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=False,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        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=(1333, 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(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py

+_base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://resnest101',
+    backbone=dict(stem_channels=128, depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    pretrained='open-mmlab://resnest50',
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch'),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg)))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=False,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        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=(1333, 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(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py

+_base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://resnest101',
+    backbone=dict(stem_channels=128, depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    pretrained='open-mmlab://resnest50',
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch'),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg),
+        mask_head=dict(norm_cfg=norm_cfg)))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromWebcam'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        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', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromWebcam'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 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(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

openmmlab_test/mmdetection-speed_xinpian/configs/resnest/metafile.yml

+Collections:
+  - Name: ResNeSt
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - ResNeSt
+    Paper: https://arxiv.org/abs/2004.08955
+    README: configs/renest/README.md
+
+Models:
+  - Name: faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.8
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth
+
+  - Name: faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth
+
+  - Name: mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth
+
+  - Name: mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth
+
+  - Name: cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth
+
+  - Name: cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.4
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth
+
+  - Name: cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth
+
+  - Name: cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: ResNeSt
+    Config: configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.5
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth

openmmlab_test/mmdetection-speed_xinpian/configs/retinanet/README.md

+# Focal Loss for Dense Object Detection
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```latex
+@inproceedings{lin2017focal,
+  title={Focal loss for dense object detection},
+  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
+  booktitle={Proceedings of the IEEE international conference on computer vision},
+  year={2017}
+}
+```
+
+## Results and models
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download  |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
+|    R-50-FPN     |  caffe  |   1x    |   3.5    |      18.6      |  36.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) |
+|    R-50-FPN     | pytorch |   1x    |   3.8    |      19.0      |  36.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) |
+|    R-50-FPN     | pytorch |   2x    |    -     |       -        |  37.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) |
+|    R-101-FPN    |  caffe  |   1x    |   5.5    |      14.7      |  38.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) |
+|    R-101-FPN    | pytorch |   1x    |   5.7    |      15.0      |  38.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) |
+|    R-101-FPN    | pytorch |   2x    |    -     |       -        |  38.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) |
+| X-101-32x4d-FPN | pytorch |   1x    |   7.0    |      12.1      |  39.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) |
+| X-101-32x4d-FPN | pytorch |   2x    |    -     |       -        |  40.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) |
+| X-101-64x4d-FPN | pytorch |   1x    |   10.0   |      8.7       |  41.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) |
+| X-101-64x4d-FPN | pytorch |   2x    |    -     |       -        |  40.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) |

openmmlab_test/mmdetection-speed_xinpian/configs/retinanet/metafile.yml

+Collections:
+  - Name: RetinaNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - Focal Loss
+        - FPN
+        - ResNet
+    Paper: https://arxiv.org/abs/1708.02002
+    README: configs/retinanet/README.md
+
+Models:
+  - Name: retinanet_r50_caffe_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.5
+      inference time (s/im): 0.05376
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth
+
+  - Name: retinanet_r50_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      inference time (s/im): 0.05263
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth
+
+  - Name: retinanet_r50_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth
+
+  - Name: retinanet_r101_caffe_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      inference time (s/im): 0.06803
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth
+
+  - Name: retinanet_r101_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.7
+      inference time (s/im): 0.06667
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth
+
+  - Name: retinanet_r101_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth
+
+  - Name: retinanet_x101_32x4d_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (s/im): 0.08264
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth
+
+  - Name: retinanet_x101_32x4d_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth
+
+  - Name: retinanet_x101_64x4d_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.0
+      inference time (s/im): 0.11494
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth
+
+  - Name: retinanet_x101_64x4d_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth