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)

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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