• RepPoints V2: Verification Meets Regression for Object Detection
• • Introduction
  • Main Results
  • • RepPoints V2

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