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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.
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.
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 about the Inception V3 model, a deep learning model for image classification based on Convolutional Neural Networks. See how it is better than previous versions and what are its optimizations and advantages.
Jun 12, 2024 · Inception v3 is an image recognition model that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. The model is the culmination of many ideas developed by...
