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Object Detection

This work is described in the technical report on ArXiv.

Introduction

This branch works with PyTorch 1.1 or higher.

This work is an open source object detection toolbox based on PyTorch. You can see the orginal work in the open-mmlab project developed by Multimedia Laboratory, CUHK.

demo image

Major features

  • Modular Design

    We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

  • Support of multiple frameworks out of box

    The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.

  • High efficiency

    All basic bbox and mask operations run on GPUs now. The training speed is faster than or comparable to other codebases, including Detectron, maskrcnn-benchmark and SimpleDet.

  • State of the art

    The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward.

Apart from MMDetection, we also released a library mmcv for computer vision research, which is heavily depended on by this toolbox.

License

This project is released under the Apache 2.0 license.

Benchmark and model zoo

Supported methods and backbones are shown in the below table. Results and models are available in the Model zoo.

ResNet ResNeXt SENet VGG HRNet
RPN ✓ ✓ ☐ ✗ ✓
Fast R-CNN ✓ ✓ ☐ ✗ ✓
Faster R-CNN ✓ ✓ ☐ ✗ ✓
Mask R-CNN ✓ ✓ ☐ ✗ ✓
Cascade R-CNN ✓ ✓ ☐ ✗ ✓
Cascade Mask R-CNN ✓ ✓ ☐ ✗ ✓
SSD ✗ ✗ ✗ ✓ ✗
RetinaNet ✓ ✓ ☐ ✗ ✓
GHM ✓ ✓ ☐ ✗ ✓
Mask Scoring R-CNN ✓ ✓ ☐ ✗ ✓
FCOS ✓ ✓ ☐ ✗ ✓
Double-Head R-CNN ✓ ✓ ☐ ✗ ✓
Grid R-CNN (Plus) ✓ ✓ ☐ ✗ ✓
Hybrid Task Cascade ✓ ✓ ☐ ✗ ✓
Libra R-CNN ✓ ✓ ☐ ✗ ✓
Guided Anchoring ✓ ✓ ☐ ✗ ✓

Other features

  • DCNv2
  • Group Normalization
  • Weight Standardization
  • OHEM
  • Soft-NMS
  • Generalized Attention
  • GCNet
  • Mixed Precision (FP16) Training

Installation

Please refer to INSTALL.md for installation and dataset preparation.

Get Started

Please see GETTING_STARTED.md for the basic usage.