RepPoints V2: Verification Meets Regression for Object Detection
本项目由于mindspore不支持cunmmax算子,因此我们对cummax进行了重写。本项目包含已修改的前行传播代码以及我们重写的cummax算子。
Introduction
Verification and regression are two general methodologies for prediction in neural networks. Each has its own strengths: verification can be easier to infer accurately, and regression is more efficient and applicable to continuous target variables. Hence, it is often beneficial to carefully combine them to take advantage of their benefits. We introduce verification tasks into the localization prediction of RepPoints, producing RepPoints v2.
RepPoints v2 aims for object detection and it achieves 52.1 bbox mAP
on COCO test-dev by a single model. Dense RepPoints v2 aims for instance segmentation and it achieves 45.9 bbox mAP
and 39.0 mask mAP
on COCO test-dev by using a ResNet-50 model.
Main Results
RepPoints V2
ResNe(X)ts:
Model |
Multi-scale training |
AP (minival) |
AP (test-dev) |
Link |
RepPoints_V2_R_50_FPN_1x |
No |
40.9 |
--- |
Google / Baidu / Log |
RepPoints_V2_R_50_FPN_GIoU_1x |
No |
41.1 |
41.3 |
Google / Baidu / Log |
RepPoints_V2_R_50_FPN_GIoU_2x |
Yes |
43.9 |
44.4 |
Google / Baidu / Log |
RepPoints_V2_R_101_FPN_GIoU_2x |
Yes |
45.8 |
46 |
Google / Baidu / Log |
RepPoints_V2_R_101_FPN_dcnv2_GIoU_2x |
Yes |
47.7 |
48.1 |
Google / Baidu / Log |
RepPoints_V2_X_101_FPN_GIoU_2x |
Yes |
47.3 |
47.8 |
Google / Baidu / Log |
RepPoints_V2_X_101_FPN_dcnv2_GIoU_2x |
Yes |
49.3 |
49.4 |
Google / Baidu / Log |