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- Understanding GoogLeNet Model - CNN Architecture - GeeksforGeeks
GoogLeNet (Inception V1) is a deep convolutional neural network architecture designed for efficient image classification It introduces the Inception module, which performs multiple convolution operations (1x1, 3x3, 5x5) in parallel, along with max pooling and concatenates their outputs
- Inception (deep learning architecture) - Wikipedia
Inception[1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1)
- [1409. 4842] Going Deeper with Convolutions - arXiv. org
One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection
- GoogLeNet: A Deep Dive into Google’s Neural Network Technology
In GoogLeNet, global average pooling can be found at the end of the network, where it summarises the features learned by the CNN and then feeds it directly into the SoftMax classifier
- GoogLeNet: Revolutionizing Deep Learning with Inception - Viso
GoogLeNet is an Image Classification model that is made by stacking Inception Modules Released in 2014, it surpassed previous benchmarks
- GoogLeNet – PyTorch
GoogLeNet was based on 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)
- GoogLeNet - Hugging Face Community Computer Vision Course
In this chapter we will go through a convolutional architecture called GoogleNet The Inception architecture, a convolutional neural network (CNN) designed for tasks in computer vision such as classification and detection, stands out due to its efficiency
- GoogLeNet Explained: From Theory to Implementation in PyTorch . . .
GoogLeNet is a Convolutional Neural Network (CNN) architecture developed by Google’s research team and introduced in the paper “Going Deeper with Convolutions” at CVPR 2015
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