Inception
A family of CNN architectures proposed by Google whose core component is the Inception module, which applies parallel convolution kernels of multiple scales at the same layer to capture features at different granularities, achieving high accuracy at relatively low computational cost.
Timeline (last 90 days)
A study systematically compared the performance of five CNN architectures—AlexNet, Inception, LeNet, VGG16 and MobileNet—in detecting twig blight of the Pequi tree (Caryocar brasiliense).
Inception introduces multi-scale parallel convolutions to capture features of different granularities at the same layer.
The Inception module simultaneously uses convolution kernels of different sizes and pooling operations at the same layer, then concatenates the outputs of each branch along the channel dimension, thereby capturing both fine-grained textures and coarse-grained shapes within a single layer.
Since Google Photos launched in 2015, early versions primarily relied on convolutional neural networks (CNNs) for image classification and object detection, using the Inception architecture.
All Facts (4)
Inception introduces multi-scale parallel convolutions to capture features of different granularities at the same layer.
50%UnverifiedThe Inception module simultaneously uses convolution kernels of different sizes and pooling operations at the same layer, then concatenates the outputs of each branch along the channel dimension, thereby capturing both fine-grained textures and coarse-grained shapes within a single layer.
50%UnverifiedSince Google Photos launched in 2015, early versions primarily relied on convolutional neural networks (CNNs) for image classification and object detection, using the Inception architecture.
50%UnverifiedA study systematically compared the performance of five CNN architectures—AlexNet, Inception, LeNet, VGG16 and MobileNet—in detecting twig blight of the Pequi tree (Caryocar brasiliense).
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