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Inceptionv3 is a CNN for image analysis and object detection, and the third edition of Google's Inception network. It has under 25 million parameters, and uses factorized convolutions, RMSProp, BatchNorm, and label smoothing to improve performance.
Inception_v3 is a ConvNet trained on ImageNet from 2015 by Google. It has a low computational cost and a high accuracy for image recognition. Learn how to use it with PyTorch, see the model description and references.
Inception v3 is a model from the Inception family that improves on the previous versions with label smoothing, factorized convolutions, and auxiliary classifier. It has 24 million parameters, 7 billion FLOPs, and achieves 77.46% top 1 accuracy on ImageNet.
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 and has been applied to various tasks and datasets.
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.
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...
