In recent years, deep learning methods have been successfully applied to image classification tasks. Many such deep neural networks exist today that can easily differentiate cats from dogs. One such model is the ResNeXt model that uses a homogeneous, multi-branch architecture for image classification. This paper aims at implementing and evaluating the ResNeXt model architecture on subsets of the CIFAR-10 dataset. It also tweaks the original ResNeXt hyper-parameters such as cardinality, depth and base-width and compares the performance of the modified model with the original. Analysis of the experiments performed in this paper show that a slight decrease in depth or base-width does not affect the performance of the model much leading to comparable results.
Soon after, @cite_18 networks were proposed by Oxford and they were the first to use much smaller 3x3 filters in each convolutional layers and also combined them as a sequence of convolutions. Instead of the 9x9 or 11x11 filters of AlexNet, filters started to become smaller, too dangerously close to the infamous 1x1 convolutions that LeNet wanted to avoid, at least on the first layers of the network. But the great advantage of VGG was the insight that multiple 3x3 convolutions in sequence can emulate the effect of larger receptive fields, for example 5x5 and 7x7. These ideas were later to be used in more recent network architectures such as Inception and ResNet.
@cite_18
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In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.