Contents
How many parameters does ImageNet have?
60 million parameters
The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
How many parameters does ResNet 152 have?
AlexNet and ResNet-152, both have about 60M parameters but there is about a 10% difference in their top-5 accuracy.
What is ImageNet ResNet?
ImageNet, is a dataset of over 15 millions labeled high-resolution images with around 22,000 categories. ILSVRC uses a subset of ImageNet of around 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images and 100,000 testing images.
What is resnet152?
ResNet, short for Residual Networks is a classic neural network used as a backbone for many computer vision tasks. This model was the winner of ImageNet challenge in 2015. In this blog we will code a ResNet-50 that is a smaller version of ResNet 152 and frequently used as a starting point for transfer learning.
What did Microsoft researchers win in ImageNet challenge?
The contests, organized by researchers from top universities and corporations, have in the past few years become a leading barometer of success in this exploding field. In the ImageNet challenge, the Microsoft team won first place in all three categories it entered: classification, localization and detection.
What was the error rate of ImageNet before 2012?
Prior to 2012, the image classification model error rate was around 25% but AlexNet shockingly surpassed that error rate with 15.3 in the 2012 ImageNet challenge. AlexNet is often regarded as the pioneer of the convolutional neural network and starting point of the Deep Learning boom.
How are image classification models trained in ImageNet?
Building upon this idea of training image classification models on ImageNet Dataset, in 2010 annual image classification competition was launched known as ImageNet Large Scale Visual Recognition Challenge or ILSVRC. ILSVRC uses the smaller portion of the ImageNet consisting of only 1000 categories.
Where did the idea for ImageNet come from?
The idea of the ImageNet visual database was conceived by Fei-Fei Li, a Professor of Computer Science at Stanford University in 2006. In those days, she was working in the field of medical imaging and faced issues in designing machine learning models due to a lack of quality images.