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Object Detection Progress Tracker

PRs Welcome

Last few years have seen great progress made in the domain of Object Detection. With powerful classification networks as backbone, more and more well-designed detection heads have been proposed to handle the dilemma of recognition and localization. This repo serves as a tracker of these progress.

2-stage

Model Backbone Tricks VOC COCO FPS Paper Date Note Code
R-CNN VGG16 - 58.5 - - Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation - CVPR2014 Oral MATLAB
Fast R-CNN VGG16 - 70.0 19.7 - Fast R-CNN 15.04 CVPR2014 Oral caffe
Faster R-CNN VGG16 - 73.2 - - Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks 15.06 NIPS2015 MATLAB
Faster R-CNN+++ ResNet-101-C4 1,2,3 73.8 34.9 - Deep Residual Learning for Image Recognition 15.12 CVPR2016 Best Paper
R-FCN ResNet-101 4 80.5 29.9 6 R-FCN: Object Detection via Region-based Fully Convoluational Networks 16.05 NIPS2016 caffe
Deformable Conv Aligned-Inception-ResNet 1,3 - 37.5 - Deformable Convolutional Networks 17.03 ICCV2017 Oral MXNet
Faster R-CNN w FPN ResNet-101-FPN - - 36.2 - Feature Pyramid Networks for Object Detection 16.12 CVPR2017 Poster caffe2
Faster R-CNN by G-RMI Inception-ResNet-v2 - - 34.7 - Speed/accuracy Trade-offs for Modern Convolutional object detectors - COCO206 winner TesnsorFlow
Mask R-CNN ResNeXt 6 - 39.8 - Mask R-CNN 17.03 ICCV2017 Best Paper caffe2
Light-haed R-CNN ResNet-101-FPN 4,6 - 41.5 - Light-Head R-CNN: In Defense of Two-stage Object Detector 17.11 -
Light-haed R-CNN Xception - - 30.7 102 Light-Head R-CNN: In Defense of Two-stage Object Detector 17.11 -

1-stage

Model Backbone Tricks VOC COCO FPS Paper Date Note Code
YOLO Custom - 63.4 - 45 YOLO: You Only Look Once - CVPR2016 Oral darknet
SSD500 VGG16 - 75.1 24.4 23 SSD: Single Shot Detector 15.12 ECCV2016 Oral caffe
SSD ResNet-101 - - 31.2 8 DSSD: Deconvolutional Single Shot Detector 17.01 -
YOLO v2 DarkNet-19 - 78.6 21.6 40 YOLO9000: Better, Faster, Stronger - CVPR2017 darknet
DSSD ResNet-101 - - 33.2 6 DSSD: Deconvolutional Single Shot Detector 17.01 -
RON384++ VGG16 3,7,8 77.6 27.4 - RON: Reverse Connection with Objectness Prior Networks for Object Detection - CVPR2017 caffe
RetinaNet ResNet-101-FPN - - 39.1 - Focal Loss for Dense Object Detection 17.08 ICCV2017 Best student paper caffe2
RefineDet512 VGG16 - 81.8 33.0 24.1 Single-Shot Refinement Neural Network for Object Detection 17.11 - caffe
RefineDet512+ ResNet-101 3 - 41.8 - Single-Shot Refinement Neural Network for Object Detection 17.11 - caffe

Tricks list

  1. box refinement
  2. context
  3. multi-scale testing
  4. multi-scale training
  5. OHEM
  6. RoIAlign
  7. box voting
  8. flip

To be added

  • FSSD
  • DSOD
  • GRP-DSOD
  • [ ]

Disclaimer:

  1. Pascal VOC AP results are evaluated with IOU 0.5. Models are trained on VOC07+12, tested on VOC07.
  2. MS COCO results are reported with training on trainval35k, testing on test-dev set.
  3. All code repos are official implementation.
  4. Reported FPS are usually on GPU, not comparable actually.

My Notes(in Chinese) can be found here.

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