Add detectron2 wrapper (#132)

* add detectron2 wrapper
zhanghang1989 · Jan 28, 2021 · 0b75086 · 0b75086
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diff --git a/README.md b/README.md
@@ -351,7 +351,7 @@ python verify.py --model resnest50 --crop-size 224
 For object detection and instance segmentation models, please visit our [detectron2-ResNeSt fork](https://github.com/zhanghang1989/detectron2-ResNeSt).
 
 ### Semantic Segmentation
- 
+
 - Training with PyTorch: [Encoding Toolkit](https://hangzhang.org/PyTorch-Encoding/model_zoo/segmentation.html).
 - Training with MXNet: [GluonCV Toolkit](https://gluon-cv.mxnet.io/model_zoo/segmentation.html#ade20k-dataset).
 

diff --git a/d2/README.md b/d2/README.md
@@ -0,0 +1,213 @@
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/instance-segmentation-on-coco)](https://paperswithcode.com/sota/instance-segmentation-on-coco?p=resnest-split-attention-networks)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/object-detection-on-coco)](https://paperswithcode.com/sota/object-detection-on-coco?p=resnest-split-attention-networks)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/panoptic-segmentation-on-coco-panoptic)](https://paperswithcode.com/sota/panoptic-segmentation-on-coco-panoptic?p=resnest-split-attention-networks)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/instance-segmentation-on-coco-minival)](https://paperswithcode.com/sota/instance-segmentation-on-coco-minival?p=resnest-split-attention-networks)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/object-detection-on-coco-minival)](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=resnest-split-attention-networks)
+
+# ResNeSt (Detectron2 Wrapper)
+
+Code for detection and instance segmentation experiments in [ResNeSt](https://hangzhang.org/files/resnest.pdf).
+
+
+## Training and Inference
+Please follow [INSTALL.md](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md) to install detectron2. 
+
+To train a model with 8 gpus, please run
+```shell
+python train_net.py  --num-gpus 8 --config-file your_config.yaml
+```
+
+For inference
+```shell
+python train_net.py  \
+    --config-file your_config.yaml
+    --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
+```
+
+For the inference demo, please see [GETTING_STARTED.md](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md).
+
+## Pretrained Models
+
+### Object Detection
+<table class="tg">
+  <tr>
+    <th class="tg-0pky">Method</th>
+    <th class="tg-0pky">Backbone</th>
+    <th class="tg-0pky">mAP%</th>
+    <th class="tg-0pky">download</th>
+  </tr>
+  <tr>
+    <td rowspan="5" class="tg-0pky">Faster R-CNN</td>
+    <td class="tg-0pky">ResNet-50</td>
+    <td class="tg-0pky">39.25</td>
+    <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_rcnn_R_50_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_R_50_FPN_syncbn_range-scale_1x-fde56e2b.pth ">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_R_50_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNet-101</td>
+    <td class="tg-0lax">41.37</td>
+     <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_rcnn_R_101_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_R_101_FPN_syncbn_range-scale_1x-57c73356.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_R_101_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-50 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>42.33</b></td>
+     <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_rcnn_ResNeSt_50_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_ResNeSt_50_FPN_syncbn_range-scale_1x-ad123c0b.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_ResNeSt_50_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-50-DCNv2 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>44.11</b></td>
+     <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_rcnn_ResNeSt_50_FPN_dcn_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_ResNeSt_50_FPN_dcn_syncbn_range-scale_1x.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_ResNeSt_50_FPN_dcn_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr> 
+  <tr>
+    <td class="tg-0lax">ResNeSt-101 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>44.72</b></td>
+    <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x-d8f284b6.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td rowspan="5" class="tg-0lax">Cascade R-CNN</td>
+    <td class="tg-0lax">ResNet-50</td>
+    <td class="tg-0lax">42.52</td>
+    <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_cascade_rcnn_R_50_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_R_50_FPN_syncbn_range-scale_1x-3c7f2ef2.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_R_50_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNet-101</td>
+    <td class="tg-0lax">44.03</td>
+    <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_cascade_rcnn_R_101_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_R_101_FPN_syncbn_range-scale_1x-4073359b.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_R_101_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-50 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>45.41</b></td>
+    <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_cascade_rcnn_ResNeSt_50_FPN_syncbn_range-scale-1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_ResNeSt_50_FPN_syncbn_range-scale-1x-e9955232.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_ResNeSt_50_FPN_syncbn_range-scale-1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-101 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>47.50</b></td>
+    <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_cascade_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x-3627ef78.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-200 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>49.03</b></td>
+    <td class="tg-0lax"><a href="./configs/COCO-Detection/faster_cascade_rcnn_ResNeSt_200_FPN_syncbn_range-scale_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_ResNeSt_200_FPN_syncbn_range-scale_1x-1be2a87e.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/faster_cascade_rcnn_ResNeSt_200_FPN_syncbn_range-scale_1x.txt">log</a> </td>
+  </tr>
+</table>
+
+We train all models with FPN, SyncBN and image scale augmentation (short size of a image is pickedrandomly from 640 to 800). 1x learning rate schedule is used. All of them are reported on COCO-2017 validation dataset.
+
+
+
+### Instance Segmentation
+<table class="tg">
+  <tr>
+    <th class="tg-0pky">Method</th>
+    <th class="tg-0pky">Backbone</th>
+    <th class="tg-0pky">bbox</th>
+    <th class="tg-0lax">mask</th>
+    <th class="tg-0pky">download</th>
+  </tr>
+  <tr>
+    <td rowspan="4" class="tg-0pky">Mask R-CNN</td>
+    <td class="tg-0pky">ResNet-50</td>
+    <td class="tg-0pky">39.97</td>
+    <td class="tg-0lax">36.05</td>
+    <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_syncbn_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_R_50_FPN_syncbn_1x-4939bd58.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_R_50_FPN_syncbn_1x.txt">log</a> </td>
+</tr>
+  <tr>
+    <td class="tg-0lax">ResNet-101</td>
+    <td class="tg-0lax">41.78</td>
+    <td class="tg-0lax">37.51</td>
+    <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_syncbn_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_R_101_FPN_syncbn_1x-55493cc2.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_R_101_FPN_syncbn_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-50 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>42.81</b></td>
+    <td class="tg-0lax"><b>38.14</td>
+    <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_rcnn_ResNeSt_50_FPN_syncBN_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_ResNeSt_50_FPN_syncBN_1x-f442d863.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_ResNeSt_50_FPN_syncBN_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-101 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>45.75</b></td>
+    <td class="tg-0lax"><b>40.65</b></td>
+     <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_rcnn_ResNeSt_101_FPN_syncBN_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_ResNeSt_101_FPN_syncBN_1x-528502c6.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_rcnn_ResNeSt_101_FPN_syncBN_1x.txt">log</a> </td>   
+  </tr>
+  <tr>
+    <td rowspan="7" class="tg-0lax">Cascade R-CNN</td>
+    <td class="tg-0lax">ResNet-50</td>
+    <td class="tg-0lax">43.06</td>
+    <td class="tg-0lax">37.19</td>
+    <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_cascade_rcnn_R_50_FPN_syncbn_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_R_50_FPN_syncbn_1x-03310c9b.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_R_50_FPN_syncbn_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNet-101</td>
+    <td class="tg-0lax">44.79</td>
+    <td class="tg-0lax">38.52</td>
+    <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_cascade_rcnn_R_101_FPN_syncbn_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_R_101_FPN_syncbn_1x-8cec1631.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_R_101_FPN_syncbn_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-50 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>46.19</b></td>
+    <td class="tg-0lax"><b>39.55</b></td>
+    <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_cascade_rcnn_ResNeSt_50_FPN_syncBN_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_50_FPN_syncBN_1x-c58bd325.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_50_FPN_syncBN_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-101 (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>48.30</b></td>
+    <td class="tg-0lax"><b>41.56</b></td>
+     <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_cascade_rcnn_ResNeSt_101_FPN_syncBN_1x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_101_FPN_syncBN_1x-62448b9c.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_101_FPN_syncBN_1x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax">ResNeSt-200-tricks-3x (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>50.54</b></td>
+    <td class="tg-0lax"><b>44.21</b></td>
+     <td class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_cascade_rcnn_ResNeSt_200_FPN_syncBN_all_tricks_3x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_200_FPN_syncBN_all_tricks_3x.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_200_FPN_syncBN_all_tricks_3x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td rowspan="2" class="tg-0lax">ResNeSt-200-dcn-tricks-3x (<span style="color:red">ours</span>)</td>
+    <td class="tg-0lax"><b>50.91</b></td>
+    <td class="tg-0lax"><b>44.50</b></td>
+     <td rowspan="2"class="tg-0lax"><a href="./configs/COCO-InstanceSegmentation/mask_cascade_rcnn_ResNeSt_200_FPN_dcn_syncBN_all_tricks_3x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_200_FPN_dcn_syncBN_all_tricks_3x-e1901134.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/mask_cascade_rcnn_ResNeSt_200_FPN_dcn_syncBN_all_tricks_3x.txt">log</a> </td>
+  </tr>
+  <tr>
+    <td class="tg-0lax"><b>53.30*</b></td>
+    <td class="tg-0lax"><b>47.10*</b></td>
+  </tr>
+</table>
+
+All models are trained along with FPN and SyncBN. For data augmentation,input images’ shorter side are randomly scaled to one of (640, 672, 704, 736, 768, 800). 1x learning rate schedule is used, if not otherwise specified. All of them are reported on COCO-2017 validation dataset. The values with * demonstrate the mutli-scale testing performance on the test-dev2019.
+
+
+
+### Panoptic Segmentation
+<table class="tg">
+  <tr>
+    <th class="tg-0pky">Backbone</th>
+    <th class="tg-0pky">bbox</th>
+    <th class="tg-0lax">mask</th>
+    <th class="tg-0lax">PQ</th>
+    <th class="tg-0pky">download</th>
+  </tr>
+  <tr>
+    <td class="tg-0pky">ResNeSt-200</td>
+    <td class="tg-0pky">51.00</td>
+    <td class="tg-0lax">43.68</td>
+    <td class="tg-0lax">47.90</td>
+    <td class="tg-0lax"><a href="./configs/COCO-PanopticSegmentation/panoptic_ResNeSt_200_FPN_syncBN_tricks_3x.yaml">config</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/panoptic_ResNeSt_200_FPN_syncBN_tricks_3x-43f8b731.pth">model</a> | <a href="https://s3.us-west-1.wasabisys.com/resnest/detectron/panoptic_ResNeSt_200_FPN_syncBN_tricks_3x.txt">log</a> </td>
+</tr> 
+</table>
+
+
+## Reference
+
+**ResNeSt: Split-Attention Networks** [[arXiv](https://arxiv.org/pdf/2004.08955.pdf)]
+
+Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li and Alex Smola
+
+```
+@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}
+}
+```
+
+### Contributors
+[Chongruo Wu](https://github.com/chongruo), [Zhongyue Zhang](http://zhongyuezhang.com/), [Hang Zhang](https://hangzhang.org/)
diff --git a/d2/configs/Base-RCNN-FPN.yaml b/d2/configs/Base-RCNN-FPN.yaml
@@ -0,0 +1,42 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  BACKBONE:
+    NAME: "build_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+  FPN:
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+  ANCHOR_GENERATOR:
+    SIZES: [[32], [64], [128], [256], [512]]  # One size for each in feature map
+    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
+  RPN:
+    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
+    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
+    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
+    # Detectron1 uses 2000 proposals per-batch,
+    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
+    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
+    POST_NMS_TOPK_TRAIN: 1000
+    POST_NMS_TOPK_TEST: 1000
+  ROI_HEADS:
+    NAME: "StandardROIHeads"
+    IN_FEATURES: ["p2", "p3", "p4", "p5"]
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_FC: 2
+    POOLER_RESOLUTION: 7
+  ROI_MASK_HEAD:
+    NAME: "MaskRCNNConvUpsampleHead"
+    NUM_CONV: 4
+    POOLER_RESOLUTION: 14
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2
diff --git a/d2/configs/COCO-Detection/faster_cascade_rcnn_R_101_FPN_syncbn_range-scale_1x.yaml b/d2/configs/COCO-Detection/faster_cascade_rcnn_R_101_FPN_syncbn_range-scale_1x.yaml
@@ -0,0 +1,30 @@
+_BASE_: "../Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
+  MASK_ON: False
+  RESNETS:
+    DEPTH: 101
+    NORM: "SyncBN"
+  FPN:
+    NORM: "SyncBN"
+  ROI_HEADS:
+    NAME: CascadeROIHeads
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_CONV: 4
+    NUM_FC: 1
+    NORM: "SyncBN"
+    CLS_AGNOSTIC_BBOX_REG: True
+  RPN: 
+    POST_NMS_TOPK_TRAIN: 2000
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02    
+INPUT:
+  MIN_SIZE_TRAIN: (640, 800)
+  MIN_SIZE_TRAIN_SAMPLING: "range"
+  MAX_SIZE_TRAIN: 1333
+TEST:
+  PRECISE_BN:
+    ENABLED: True
+
diff --git a/d2/configs/COCO-Detection/faster_cascade_rcnn_R_50_FPN_syncbn_range-scale_1x.yaml b/d2/configs/COCO-Detection/faster_cascade_rcnn_R_50_FPN_syncbn_range-scale_1x.yaml
@@ -0,0 +1,30 @@
+_BASE_: "../Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  MASK_ON: False
+  RESNETS:
+    DEPTH: 50
+    NORM: "SyncBN"
+  FPN:
+    NORM: "SyncBN"
+  ROI_HEADS:
+    NAME: CascadeROIHeads
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_CONV: 4
+    NUM_FC: 1
+    NORM: "SyncBN"
+    CLS_AGNOSTIC_BBOX_REG: True
+  RPN:
+    POST_NMS_TOPK_TRAIN: 2000
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02    
+INPUT:
+  MIN_SIZE_TRAIN: (640, 800)
+  MIN_SIZE_TRAIN_SAMPLING: "range"
+  MAX_SIZE_TRAIN: 1333
+TEST:
+  PRECISE_BN:
+    ENABLED: True
+
diff --git a/d2/configs/COCO-Detection/faster_cascade_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x.yaml b/d2/configs/COCO-Detection/faster_cascade_rcnn_ResNeSt_101_FPN_syncbn_range-scale_1x.yaml
@@ -0,0 +1,34 @@
+_BASE_: "../ResNest-Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "https://s3.us-west-1.wasabisys.com/resnest/detectron/resnest101_detectron-486f69a8.pth"
+  MASK_ON: False
+  RESNETS:
+    DEPTH: 101
+    STRIDE_IN_1X1: False
+    RADIX: 2
+    NORM: "SyncBN"
+  FPN:
+    NORM: "SyncBN"
+  ROI_HEADS:
+    NAME: CascadeROIHeads
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_CONV: 4
+    NUM_FC: 1
+    NORM: "SyncBN"
+    CLS_AGNOSTIC_BBOX_REG: True
+  RPN:
+    POST_NMS_TOPK_TRAIN: 2000
+  PIXEL_MEAN: [123.68, 116.779, 103.939]
+  PIXEL_STD: [58.393, 57.12, 57.375]
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02    
+INPUT:
+  MIN_SIZE_TRAIN: (640, 800)
+  MIN_SIZE_TRAIN_SAMPLING: "range"
+  MAX_SIZE_TRAIN: 1333
+  FORMAT: "RGB"
+TEST:
+  PRECISE_BN:
+    ENABLED: True
diff --git a/d2/configs/COCO-Detection/faster_cascade_rcnn_ResNeSt_200_FPN_syncbn_range-scale_1x.yaml b/d2/configs/COCO-Detection/faster_cascade_rcnn_ResNeSt_200_FPN_syncbn_range-scale_1x.yaml
@@ -0,0 +1,34 @@
+_BASE_: "../ResNest-Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "https://s3.us-west-1.wasabisys.com/resnest/detectron/resnest200_detectron-02644020.pth"
+  MASK_ON: False
+  RESNETS:
+    DEPTH: 200
+    STRIDE_IN_1X1: False
+    RADIX: 2
+    NORM: "SyncBN"
+  FPN:
+    NORM: "SyncBN"
+  ROI_HEADS:
+    NAME: CascadeROIHeads
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_CONV: 4
+    NUM_FC: 1
+    NORM: "SyncBN"
+    CLS_AGNOSTIC_BBOX_REG: True
+  RPN:
+    POST_NMS_TOPK_TRAIN: 2000
+  PIXEL_MEAN: [123.68, 116.779, 103.939]
+  PIXEL_STD: [58.393, 57.12, 57.375]
+SOLVER:
+  IMS_PER_BATCH: 16  
+  BASE_LR: 0.02    
+INPUT:
+  MIN_SIZE_TRAIN: (640, 800)
+  MIN_SIZE_TRAIN_SAMPLING: "range"
+  MAX_SIZE_TRAIN: 1333
+  FORMAT: "RGB"
+TEST:
+  PRECISE_BN:
+    ENABLED: True