In this story, ResNet  is reviewed. ResNet can have a very deep network of up to layers by learning the residual representation functions instead of learning the signal representation directly.
ResNet introduces skip connection or shortcut connection to fit the input from the previous layer to the next layer without any modification of the input. This is a CVPR paper with more than citations. Sik-Ho Tsang Medium. ImageNet, is a dataset of over 15 millions labeled high-resolution images with around 22, categories.
In all, there are roughly 1. When the network is deep, and multiplying n of these small numbers will become zero vanished. When the network is deep, and multiplying n of these large numbers will become too large exploded. We expect deeper network will have more accurate prediction. However, below shows an example, layer plain network got lower training error and test error than layer plain networka degradation problem occurs due to vanishing gradients.
Even if there is vanishing gradient for the weight layers, we always still have the identity x to transfer back to earlier layers. The above figure shows the ResNet architecture. A Shortcut performs identity mapping, with extra zero padding for increasing dimensions.
Thus, no extra parameters. B The projection shortcut is used for increasing dimensions only, the other shortcuts are identity. Extra parameters are needed. C All shortcuts are projections. Extra parameters are more than that of B. Since the network is very deep now, the time complexity is high. A bottleneck design is used to reduce the complexity as follows:.
It only takes a minute to sign up. In the paper on ResNetauthors say, that their layer network has lesser complexity than VGG network with 16 or 19 layers:. We construct layer and layer ResNets by using more 3-layer blocks Table 1. Remarkably, although the depth is significantly increased, the layer ResNet Sign up to join this community.
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Image classification is the task of classifying a given image into one of the pre-defined categories. Traditional pipeline for image classification involves two modules: viz. Feature extraction involves extracting a higher level of information from raw pixel values that can capture the distinction among the categories involved. This feature extraction is done in an unsupervised manner wherein the classes of the image have nothing to do with information extracted from pixels. After the feature is extracted, a classification module is trained with the images and their associated labels.
The problem with this pipeline is that feature extraction cannot be tweaked according to the classes and images.
So if the chosen feature lacks the representation required to distinguish the categories, the accuracy of the classification model suffers a lot, irrespective of the type of classification strategy employed. A common theme among the state of the art following the traditional pipeline has been, to pick multiple feature extractors and club them inventively to get a better feature.
But this involves too many heuristics as well as manual labor to tweak parameters according to the domain to reach a decent level of accuracy. By decent I mean, reaching close to human level accuracy.
We once produced better results using ConvNets for a company a client of my start-up in 6 weeks, which took them close to a year to achieve using traditional computer vision. Another problem with this method is that it is completely different from how we humans learn to recognize things.
Just after birth, a child is incapable of perceiving his surroundings, but as he progresses and processes data, he learns to identify things. This is the philosophy behind deep learning, wherein no hard-coded feature extractor is built in.
It combines the extraction and classification modules into one integrated system and it learns to extract, by discriminating representations from the images and classify them based on supervised data.In this story, Inception-v4  by Google is reviewed.
From the below figure, we can see the top-1 accuracy from v1 to v4. And Inception-v4 is better than ResNet. Inception network with residual connections, an idea proposed by Microsoft ResNet, outperforms similarly expensive Inception network without residual connections. With ensemble of 1 Inception-v4 and 3 residual networks, 3. Sik-Ho Tsang Medium.
ReLU is used as activation function to address the saturation problem and the resulting vanishing gradients. But it also makes the output more irregular. It is advantageous for the distribution of X to remain fixed over time because a small change will be amplified when network goes deeper.Www.dungsmultiblock.com
Higher learning rate can be used. Factorization was introduced in convolution layer as shown above to further reduce the dimensionality, so as to reduce the overfitting problem. For example:. And an efficient grid size reduction module was also introduced which is less expensive and still efficient network. With the efficient grid size reduction, say for example in the figure, feature maps are done by conv with stride 2.
And these 2 sets of feature maps are concatenated as feature maps and go to the next level of inception module.
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
It only takes a minute to sign up. Number of parameters reduces amount of space required to store the network, but it doesn't mean that it's faster.
ResNet (34, 50, 101): Residual CNNs for Image Classification Tasks
Resnet is faster than VGG, but for a different reason. Also, as mrgloom pointed out that computational speed my depend heavily on the implementation. Below I'll discuss simple computational case. Also, I'll avoid counting FLOPs for activation functions and pooling layers, since they have relatively low cost. First of all, speed of the convolution will depend on the size of the input.
Let's say you have gray colored 1 channel x image and you apply one 3x3 convolution filter with stride 1 and 0 padding. It's easy to calculate. If you know how convolution works you should know that from the x image you will get 98x98 using set up described above. And in order to compute each value from the 98x98 output image you need to do 9 multiplication and 8 additions, which is in total corresponds to 17 operations per value.
Now, imagine you apply the same filter on the larger image, let's say x Image has 4 times bigger area and therefor you'll get roughly 4 times more FLOPs. Now, I'll start with the comparison between VGG19 and Resnet 34, since that's the image that they use in the original paper.
In the figure 3, they break down architecture into the set of blocks marked with different colors. At the end each block reduces height and width by a factor of two.Pytorch einsum speed
In the first two layers Resnet manages to reduces hight and width of the image by a factor of 4. From the VGG19 you can see that first two layers apply convolution on top of the full x image which is quite expensive. In fact, it's almost 3.Fruit exporters
In order to avoid this computational problem in the Resnet they address this issue in the first layer.AlexNet was born out of need to improve the results of the ImageNet challenge. This was one of the first Deep convolutional network to achieve considerable accuracy on the ImageNet LSVRC challenge with an accuracy of The idea of spatial correlation in an image frame was explored using convolutional layers and receptive fields.
The structural details of each layer in the network can be found in the table below.
Review: Inception-v4 — Evolved From GoogLeNet, Merged with ResNet Idea (Image Classification)
The network has a total of 62 million trainable variables. The input to the network is a batch of RGB images of size xx3 and outputs a x1 probability vector one corresponding to each class.
The structural details of a VGG16 network has been shown below. VGG16 has a total of million parameters. The important point to note here is that all the conv kernels are of size 3x3 and maxpool kernels are of size 2x2 with stride of two. The idea behind having fixed size kernels is that all the variable size convolutional kernels used in Alexnet 11x11, 5x5, 3x3 can be replicated by making use of multiple 3x3 kernels as building blocks.
The replication is in terms of the receptive field covered by the kernels. Say we have an input layer of size 5x5x1. Implementing a conv layer with kernel size of 5x5 and stride one will result and output feature map of 1x1.Fish2sphere
The same output feature map can be obtained by implementing two 3x3 conv layers with stride of 1 as shown below. For a 5x5 conv layer filter the number of variables is Similarly, the effect of one 7x7 11x11 conv layer can be achieved by implementing three five 3x3 conv layer with stride of one. This reduces the number of trainable variables by Reduced number of trainable variables means faster learning and more robust to over-fitting. Neural Networks are notorious for not being able to find a simpler mapping when it exists.
Most commonly used ones are ResNet50 and ResNetDeep convolutional neural networks have achieved the human level image classification result. The stacked layer is of crucial importance, look at the ImageNet result. When the deeper network starts to converge, a degradation problem has been exposed: with the network depth increasing, accuracy gets saturated which might be unsurprising and then degrades rapidly.
Such degradation is not caused by overfitting or by adding more layers to a deep network leads to higher training error. The deterioration of training accuracy shows that not all systems are easy to optimize. To overcome this problem, Microsoft introduced a deep residual learning framework. Instead of hoping every few stacked layers directly fit a desired underlying mapping, they explicitly let these layers fit a residual mapping.
Shortcut connections are those skipping one or more layers shown in Figure 1. By using the residual network, there are many problems which can be solved such as:.
The images were collected from the internet and labeled by humans using a crowd-sourcing tool. There are approximately 1. It also provides a standard set of tools for accessing the data sets and annotations, enables evaluation and comparison of different methods and ran challenges evaluating performance on object class recognition.
When the dimensions increase dotted line shortcuts in Fig. For either of the options, if the shortcuts go across feature maps of two size, it performed with a stride of 2. Each ResNet block is either two layers deep used in small networks like ResNet 18, 34 or 3 layers deep ResNet 50, They use option 2 for increasing dimensions.
This model has 3. Even after the depth is increased, the layer ResNet The image is resized with its shorter side randomly sampled in [,] for scale augmentation. The learning rate starts from 0. They use a weight decay of 0. The 18 layer network is just the subspace in 34 layer network, and it still performs better. ResNet outperforms with a significant margin in case the network is deeper.
ResNet network converges faster compared to the plain counterpart of it. Figure 4 shows that the deeper ResNet achieve better training result as compared to the shallow network. ResNet achieves a top-5 validation error of 4. A combination of 6 models with different depths achieves a top-5 validation error of 3. Author: Muneeb ul Hassan.
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