Going Deeper with Convolutions

CVPR 2015 Christian SzegedyWei LiuYangqing JiaPierre SermanetScott ReedDragomir AnguelovDumitru ErhanVincent VanhouckeAndrew Rabinovich

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network... (read more)

PDF Abstract

Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Image Classification ImageNet Inception V1 Top 1 Accuracy 69.8% # 25
Image Classification ImageNet Inception V1 Top 5 Accuracy 89.9% # 18
Object Detection ImageNet Detection Inception V1 MAP 43.9% # 1