BoxInst: High-Performance Instance Segmentation with Box Annotations

Related tags

Deep LearningOpenMMLab-BoxInst
Overview

Introduction

This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge, the paper is BoxInst: High-Performance Instance Segmentation with Box Annotations

License

This project is released under the Apache 2.0 license.

Benchmark and model zoo

BoxInst

Name box AP mask AP log download
BoxInst_MS_R_50_1x 0.390 0.304 log model
BoxInst_MS_R_50_90k 0.388 0.302 log model
BoxInst_MS_R_101_90k 0.410 0.318 - model

Some other methods in MMDetection are also supported.

Getting Started

Our project is totally based on MMCV and MMDetection. Please see get_started.md for the basic usage of MMDetection.

Train

Please see doc to start training. Example,

CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh configs/boxinst/boxinst_r50_caffe_fpn_coco_mstrain_1x.py 4

please following linear linear scaling rule to adjust batch size, learning rate and iterations.

Inference and Eval

python tools/test.py configs/boxinst/boxinst_r50_caffe_fpn_coco_mstrain_1x.py work_dirs/boxinst_r50_caffe_fpn_coco_mstrain_1x.py/latest.pth --eval bbox segm

Acknowledgement

  • MMCV: OpenMMLab foundational library for computer vision.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
Owner
GitHub Repository
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