Going Deeper with Convolutions

CVPR 2015 • Christian Szegedy • Wei Liu • Yangqing Jia • Pierre Sermanet • Scott Reed • Dragomir Anguelov • Dumitru Erhan • Vincent Vanhoucke • Andrew 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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 SOTA for Object Detection on ImageNet Detection

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Image Classification ImageNet Inception V1 Top 1 Accuracy 69.8% # 103
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Image Classification ImageNet Inception V1 Top 5 Accuracy 89.9% # 80
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Image Classification ImageNet Inception V1 Number of params 5M # 1
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Object Detection ImageNet Detection Inception V1 MAP 43.9% # 1
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