Update README

configs/cfa/README.md

@@ -1,6 +1,8 @@
-# [Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection.](https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf)
+# CFA
+> [Beyond Bounding-Box: Convex-hull Feature Adaptation for Oriented and Densely Packed Object Detection.](https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Beyond_Bounding-Box_Convex-Hull_Feature_Adaptation_for_Oriented_and_Densely_Packed_CVPR_2021_paper.pdf)
 
 <!-- [ALGORITHM] -->
+
 ## Abstract
 
 <div align=center>
@@ -11,9 +13,8 @@ Detecting oriented and densely packed objects remains challenging for spatial fe
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
-#### RepPoints
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.9 | - | 2 | [rotated_reppoints_r50_fpn_1x_dota_oc](../rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json)

configs/g_reppoints/README.md

@@ -1,4 +1,5 @@
-# G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection.
+# G-Rep
+> > G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection.
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -7,9 +8,8 @@ Core code will release later.
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
-#### RepPoints
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.9 | - | 2 | [rotated_reppoints_r50_fpn_1x_dota_oc](../rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json)

configs/gliding_vertex/README.md

@@ -1,4 +1,5 @@
-# [Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection](https://arxiv.org/pdf/1911.09358.pdf)
+# Gliding Vertex
+> [Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection](https://arxiv.org/pdf/1911.09358.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -12,8 +13,7 @@ Object detection has recently experienced substantial progress. Yet, the widely
 
 ## Results and models
 
-### DOTA1.0
-
+DOTA1.0
 
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|

configs/gwd/README.md

@@ -1,4 +1,5 @@
-# [Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss](https://arxiv.org/pdf/2101.11952.pdf)
+# GWD
+> [Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss](https://arxiv.org/pdf/2101.11952.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -11,9 +12,8 @@ Boundary discontinuity and its inconsistency to the final detection metric have
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
-#### RotatedRetinaNet
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 64.55 | oc | 1x | 3.38 | 14.8 | - | 2 | [rotated_retinanet_hbb_r50_fpn_1x_dota_oc](../rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_r50_fpn_1x_dota_oc-e8a7c7df.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_r50_fpn_1x_dota_oc_20220121_095315.log.json)

configs/kfiou/README.md

@@ -1,4 +1,5 @@
-# [The KFIoU Loss for Rotated Object Detection](https://arxiv.org/pdf/2101.11952.pdf)
+# KFIoU
+> [The KFIoU Loss for Rotated Object Detection](https://arxiv.org/pdf/2101.11952.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -22,17 +23,15 @@ base detectors show the effectiveness of our approach.
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
-#### RotatedRetinaNet
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 64.55 | oc | 1x | 3.38 | 14.8 | - | 2 | [rotated_retinanet_hbb_r50_fpn_1x_dota_oc](../rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_r50_fpn_1x_dota_oc-e8a7c7df.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_r50_fpn_1x_dota_oc_20220121_095315.log.json)
 | ResNet50 (1024,1024,200) | 69.60 | le90 | 1x | 3.38 | 14.8 | - | 2 | [rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le90](./rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le90.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/kfiou/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le90/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le90-03e02f75.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/kfiou/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le90/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le90_20220209_173225.log.json)
 | ResNet50 (1024,1024,200) | 69.76 | oc | 1x | 3.39 | 15.1 | - | 2 | [rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_oc](./rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/kfiou/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_oc-c00be030.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/kfiou/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_oc_20220126_081643.log.json)
 | ResNet50 (1024,1024,200) | 69.77 | le135 | 1x | 3.38 | 15.1 | - | 2 | [rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le135](./rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le135.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/kfiou/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le135/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le135-0eaa4156.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/kfiou/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le135/rotated_retinanet_hbb_kfiou_r50_fpn_1x_dota_le135_20220209_173257.log.json)
 
-#### R3Det
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 69.80 | oc | 1x | 3.54 | 12.1 | - | 2 | [r3det_r50_fpn_1x_dota_oc](../r3det/r3det_r50_fpn_1x_dota_oc.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/r3det/r3det_r50_fpn_1x_dota_oc/r3det_r50_fpn_1x_dota_oc-b1fb045c.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/r3det/r3det_r50_fpn_1x_dota_oc/r3det_r50_fpn_1x_dota_oc_20220126_191226.log.json)

configs/kld/README.md

@@ -1,4 +1,5 @@
-# [Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence](https://arxiv.org/pdf/2106.01883.pdf)
+# KLD
+> [Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence](https://arxiv.org/pdf/2106.01883.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -11,9 +12,8 @@ Existing rotated object detectors are mostly inherited from the horizontal detec
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
-#### RotatedRetinaNet
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 64.55 | oc | 1x | 3.38 | 14.8 | - | 2 | [rotated_retinanet_hbb_r50_fpn_1x_dota_oc](../rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_r50_fpn_1x_dota_oc-e8a7c7df.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_retinanet/rotated_retinanet_hbb_r50_fpn_1x_dota_oc/rotated_retinanet_hbb_r50_fpn_1x_dota_oc_20220121_095315.log.json)
@@ -24,7 +24,7 @@ Existing rotated object detectors are mostly inherited from the horizontal detec
 | ResNet50 (1024,1024,200) | 69.80 | oc | 1x | 3.54 | 12.1 | - | 2 | [r3det_r50_fpn_1x_dota_oc](../r3det/r3det_r50_fpn_1x_dota_oc.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/r3det/r3det_r50_fpn_1x_dota_oc/r3det_r50_fpn_1x_dota_oc-b1fb045c.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/r3det/r3det_r50_fpn_1x_dota_oc/r3det_r50_fpn_1x_dota_oc_20220126_191226.log.json)
 | ResNet50 (1024,1024,200) | 71.83 | oc | 1x | 3.54 | 12.2 | - | 2 | [r3det_kld_r50_fpn_1x_dota_oc](./r3det_kld_r50_fpn_1x_dota_oc.py) |  [model](https://download.openmmlab.com/mmrotate/v0.1.0/kld/r3det_kld_r50_fpn_1x_dota_oc/r3det_kld_r50_fpn_1x_dota_oc-31866226.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/kld/r3det_kld_r50_fpn_1x_dota_oc/r3det_kld_r50_fpn_1x_dota_oc_20220210_114049.log.json)
 
-#### R3Det*
+
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 70.18 | oc | 1x | 3.23 | 15.1 | - | 2 | [r3det_tiny_r50_fpn_1x_dota_oc](../r3det/r3det_tiny_r50_fpn_1x_dota_oc.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/r3det/r3det_tiny_r50_fpn_1x_dota_oc/r3det_tiny_r50_fpn_1x_dota_oc-c98a616c.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/r3det/r3det_tiny_r50_fpn_1x_dota_oc/r3det_tiny_r50_fpn_1x_dota_oc_20220209_171624.log.json)

configs/oriented_rcnn/README.md

@@ -1,4 +1,5 @@
-# [Oriented R-CNN for Object Detection](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf)
+# Oriented R-CNN
+> [Oriented R-CNN for Object Detection](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -12,8 +13,7 @@ Current state-of-the-art two-stage detectors generate oriented proposals through
 
 ## Results and models
 
-### DOTA1.0
-
+DOTA1.0
 
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|

configs/r3det/README.md

@@ -1,4 +1,5 @@
-# [R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object](https://arxiv.org/pdf/1908.05612.pdf)
+# R3Det
+> [R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object](https://arxiv.org/pdf/1908.05612.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -11,7 +12,7 @@ Rotation detection is a challenging task due to the difficulties of locating the
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|

configs/redet/README.md

@@ -1,4 +1,5 @@
-# [ReDet: A Rotation-equivariant Detector for Aerial Object Detection](https://openaccess.thecvf.com/content/CVPR2021/papers/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.pdf)
+# ReDet
+> [ReDet: A Rotation-equivariant Detector for Aerial Object Detection](https://openaccess.thecvf.com/content/CVPR2021/papers/Han_ReDet_A_Rotation-Equivariant_Detector_for_Aerial_Object_Detection_CVPR_2021_paper.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -10,22 +11,20 @@
 Recently, object detection in aerial images has gained much attention in computer vision. Different from objects in natural images, aerial objects are often distributed with arbitrary orientation. Therefore, the detector requires more parameters to encode the orientation information, which are often highly redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model the orientation variation, large amounts of rotation augmented data is needed to train an accurate object detector. In this paper, we propose a Rotation-equivariant Detector (ReDet) to address these issues, which explicitly encodes rotation equivariance and rotation invariance. More precisely, we incorporate rotation-equivariant networks into the detector to extract rotation-equivariant features, which can accurately predict the orientation and lead to a huge reduction of model size. Based on the rotation-equivariant features, we also present Rotation-invariant RoI Align (RiRoI Align), which adaptively extracts rotation-invariant features from equivariant features according to the orientation of RoI. Extensive experiments on several challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016, show that our method can achieve state-of-the-art performance on the task of aerial object detection. Compared with previous best results, our ReDet gains 1.2, 3.5 and 2.6 mAP on DOTA-v1.0, DOTA-v1.5 and HRSC2016 respectively while reducing the number of parameters by 60% (313 Mb vs. 121 Mb).
 
 
-
-
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
 | ResNet50 (1024,1024,200) | 73.40 | le90 | 1x | 8.46 | 16.0 | - | 2 | [rotated_faster_rcnn_r50_fpn_1x_dota_le90](../rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90/rotated_faster_rcnn_r50_fpn_1x_dota_le90-0393aa5c.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90/rotated_faster_rcnn_r50_fpn_1x_dota_le90_20220131_082156.log.json)
 | ReResNet50 (1024,1024,200) | 76.68| le90 | 1x | 9.32 | 4.0 | - | 2 | [redet_re50_refpn_1x_dota_le90](./redet_re50_refpn_1x_dota_le90.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/redet/redet_re50_fpn_1x_dota_le90/redet_re50_fpn_1x_dota_le90-724ab2da.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/redet/redet_re50_fpn_1x_dota_le90/redet_re50_fpn_1x_dota_le90_20220130_132751.log.json)
 | ReResNet50 (1024,1024,200) | 79.87 | le90 | 1x |  | 4.0 | MS+RR | 2 | [redet_re50_refpn_1x_dota_ms_rr_le90](./redet_re50_refpn_1x_dota_ms_rr_le90.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/redet/redet_re50_fpn_1x_dota_ms_rr_le90/redet_re50_fpn_1x_dota_ms_rr_le90-fc9217b5.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/redet/redet_re50_fpn_1x_dota_ms_rr_le90/redet_re50_fpn_1x_dota_ms_rr_le90_20220206_105343.log.json)
 
+Notes:
 - `MS` means multiple scale image split.
 - `RR` means random rotation.
-
-Please download pretrained weight of ReResNet from [ReDet](https://github.com/csuhan/ReDet), and put it on `work_dirs/pretrain`. BTW, it is normal for `missing keys in source state_dict: xxx.filter ` to appear in the log. Don't worry!
+- Please download pretrained weight of ReResNet from [ReDet](https://github.com/csuhan/ReDet), and put it on `work_dirs/pretrain`. BTW, it is normal for `missing keys in source state_dict: xxx.filter ` to appear in the log. Don't worry!
 
 ## Citation
 ```

configs/roi_trans/README.md

@@ -1,4 +1,5 @@
-# [Learning RoI Transformer for Oriented Object Detection in Aerial Images](https://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_Learning_RoI_Transformer_for_Oriented_Object_Detection_in_Aerial_Images_CVPR_2019_paper.pdf)
+# RoI Trans
+> [Learning RoI Transformer for Oriented Object Detection in Aerial Images](https://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_Learning_RoI_Transformer_for_Oriented_Object_Detection_in_Aerial_Images_CVPR_2019_paper.pdf)
 
 <!-- [ALGORITHM] -->
 ## Abstract
@@ -11,7 +12,7 @@ Object detection in aerial images is an active yet challenging task in computer
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|
@@ -20,6 +21,7 @@ Object detection in aerial images is an active yet challenging task in computer
 | Swin-tiny (1024,1024,200) | 77.51 | le90 | 1x |   | 10.6 | - | 2 | [roi_trans_swin_tiny_fpn_1x_dota_le90](./roi_trans_swin_tiny_fpn_1x_dota_le90.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/roi_trans/roi_trans_swin_tiny_fpn_1x_dota_le90/roi_trans_swin_tiny_fpn_1x_dota_le90-ddeee9ae.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/roi_trans/roi_trans_swin_tiny_fpn_1x_dota_le90/roi_trans_swin_tiny_fpn_1x_dota_le90_20220131_083622.log.json)
 | ResNet50 (1024,1024,200) | 79.66 | le90 | 1x |   | 13.7 | MS+RR | 2 | [roi_trans_r50_fpn_1x_dota_ms_le90](./roi_trans_r50_fpn_1x_dota_ms_le90.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/roi_trans/roi_trans_r50_fpn_1x_dota_ms_rr_le90/roi_trans_r50_fpn_1x_dota_ms_rr_le90-fa99496f.pth) &#124; [log](https://download.openmmlab.com/mmrotate/v0.1.0/roi_trans/roi_trans_r50_fpn_1x_dota_ms_rr_le90/roi_trans_r50_fpn_1x_dota_ms_rr_le90_20220205_171729.log.json)
 
+Notes:
 - `MS` means multiple scale image split.
 - `RR` means random rotation.
 

configs/rotated_faster_rcnn/README.md

@@ -1,4 +1,5 @@
-# [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://papers.nips.cc/paper/2015/file/14bfa6bb14875e45bba028a21ed38046-Paper.pdf)
+# Rotated Faster R-CNN
+> [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://papers.nips.cc/paper/2015/file/14bfa6bb14875e45bba028a21ed38046-Paper.pdf)
 
 
 <!-- [ALGORITHM] -->
@@ -14,7 +15,7 @@ State-of-the-art object detection networks depend on region proposal algorithms
 
 ## Results and models
 
-### DOTA1.0
+DOTA1.0
 
 |    Backbone   |    mAP   | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
 |:------------:|:----------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:-------------:|