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.
@cite_15 were proposed in December 2015 and since then have created major improvements in accuracy in many computer vision tasks. The ResNet architecture was the first to pass human level performance on ImageNet, and their main contribution of residual learning is often used by default in many state-of-the-art networks today. Some of the key contributions of the ResNet model were as follows: 1) Showed that a naive stacking of layers to make the network very deep won’t always help and can actually make things worse. 2) To address the above issue, they introduced residual learning with skip-connections. The idea is that by using an additive skip connection as a shortcut, deep layers have direct access to features from previous layers. This allows feature information to more easily be propagated through the network. It also helps with training as the gradients can also more efficiently be back-propagated. 3) The first “ultra deep” network, where it is common to use over 100-200 layers.
@cite_15
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Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57 error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28 relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.