Object Detection Progress Tracker

---

[2019.03 Update]This repo collected stat-of-the-art results on Object Detection task before 2018.03. Since I've changed my research interest, I will not update this page anymore. A few recommended places for quickly learning about the area are:

• Object Detection Paper List(Also a great review!)
• Paper With Code Leaderboards of COCO, Pascal, etc

PS: I still recommend that you should take notice of tricks the author used and FPS as well as their hardware platforms when evaluating a method. Happy Deep Learning.

---

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

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.