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Inception v3 is a convolutional neural network for assisting in image analysis and object detection, and got its start as a module for GoogLeNet. It is the third edition of Google's Inception Convolutional Neural Network, originally introduced during the ImageNet Recognition Challenge.
Inception-v3 is a deep learning model for image classification that uses label smoothing, factorized convolutions, and an auxiliary classifier. It is part of the Inception family of networks that aim to improve efficiency and accuracy.
Inception v3: Based on the exploration of ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifer to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead).
Learn how to use the Inception v3 architecture, a deep convolutional neural network for computer vision, with Keras. You can load the model with weights pre-trained on ImageNet or fine-tune it for your own data.
Mar 11, 2023 · InceptionV3 is a convolutional neural network architecture developed by Google researchers. It was introduced in 2015 and is a successor to the original Inception architecture (InceptionV1) and...
Dec 2, 2015 · A paper that explores ways to scale up convolutional networks for computer vision tasks. It introduces a new network design called Inception-v3 that achieves state-of-the-art results on ILSVRC 2012 classification challenge.
