TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE
Object Detection	COCO test-dev	Faster R-CNN	box AP	34.7	# 100

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Speed/accuracy trade-offs for modern convolutional object detectors

CVPR 2017 • Jonathan Huang • Vivek Rathod • Chen Sun • Menglong Zhu • Anoop Korattikara • Alireza Fathi • Ian Fischer • Zbigniew Wojna • Yang song • Sergio Guadarrama • Kevin Murphy

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems... (read more)

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tensorflow/models

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IBM/MAX-Object-Detector

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Qengineering/TensorFlow_Lite_SSD_RPi_64-bits

felixlephuoc/Annotating-Images-Object-Detection-API

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OBJECT DETECTION

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Ranked #100 on Object Detection on COCO test-dev

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	RESULT	BENCHMARK
Object Detection	COCO test-dev	Faster R-CNN	box AP	34.7	# 100		Compare

Methods used in the Paper

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METHOD	TYPE
ReLU	Activation Functions
Dropout	Regularization
Dense Connections	Feedforward Networks
Max Pooling	Pooling Operations
VGG	Convolutional Neural Networks
Non Maximum Suppression	Proposal Filtering
1x1 Convolution	Convolutions
SSD	Object Detection Models
RPN	Region Proposal
Softmax	Output Functions
Position-Sensitive RoI Pooling	RoI Feature Extractors
Convolution	Convolutions
RoIPool	RoI Feature Extractors
Faster R-CNN	Object Detection Models
R-FCN	Object Detection Models

Speed/accuracy trade-offs for modern convolutional object detectors

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