AlexNet

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AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012.^[1]
AlexNet achieved a top-5 error around 16%, which was an extremely good result back in 2012.^[2]
AlexNet is made up of eight trainable layers, five convolution layers and three fully connected layers.^[2]
To address overfitting during training, AlexNet uses both data augmentation and dropout layers.^[3]
Training an image on the VGG network uses techniques similar to Krizhevsky et al., mentioned previously (i.e. the training of AlexNet).^[3]
AlexNet is one of the variants of CNN which is also referred to as a Deep Convolutional Neural Network.^[4]
Then the AlexNet applies maximum pooling layer or sub-sampling layer with a filter size 3×3 and a stride of two.^[5]
To achieve optimum optimal values of Deep Learning AlexNet structure, Modified Crow Search (MCS) is presented.^[6]
The selected Alexnet is ineffective to upgrade the model execution.^[6]
AlexNet, as a primary, average, fundamental, and a standout amongst the best DCNN engineering, was first proposed in our work.^[6]
This optimal Alexnet is anything but difficult to upgrade the quality and expanding the SR by standardized learning rates.^[6]
Following the convolutional layers, the original AlexNet had fully-connected layers with 4096 nodes each.^[7]
We saw earlier that Image Classification is a quite easy task thanks to Deep Learning nets such as Alexnet.^[8]
The convolutional part of Alexnet is used to compute the features of each region and then SVMs use these features to classify the regions.^[8]
AlexNet was the pioneer in CNN and open the whole new research era.^[9]
After competing in ImageNet Large Scale Visual Recognition Challenge, AlexNet shot to fame.^[10]
Before exploring AlexNet it is essential to understand what is a convolutional neural network.^[10]
AlexNet was trained on a GTX 580 GPU with only 3 GB of memory which couldn’t fit the entire network.^[10]
The authors of AlexNet used pooling windows, sized 3×3 with a stride of 2 between the adjacent windows.^[10]
The figure shown below shows us that with the help of ReLUs(solid curve), AlexNet can achieve a 25% training error rate.^[10]
The authors of AlexNet extracted random crops sized 227×227 from inside the 256×256 image boundary, and used this as the network’s inputs.^[10]
AlexNet was able to recognize off-center objects and most of its top 5 classes for each image were reasonable.^[10]
AlexNet was not the first fast GPU-implementation of a CNN to win an image recognition contest.^[11]
In short, AlexNet contains 5 convolutional layers and 3 fully connected layers.^[12]
AlexNet famously won the 2012 ImageNet LSVRC-2012 competition by a large margin (15.3% VS 26.2% (second place) error rates).^[13]
AlexNet uses Rectified Linear Units (ReLU) instead of the tanh function, which was standard at the time.^[14]
AlexNet allows for multi-GPU training by putting half of the model’s neurons on one GPU and the other half on another GPU.^[14]
AlexNet vastly outpaced this with a 37.5% top-1 error and a 17.0% top-5 error.^[14]
AlexNet is able to recognize off-center objects and most of its top five classes for each image are reasonable.^[14]
AlexNet is an incredibly powerful model capable of achieving high accuracies on very challenging datasets.^[14]
However, removing any of the convolutional layers will drastically degrade AlexNet’s performance.^[14]
AlexNet consists of eight layers: five convolutional layers, two fully-connected hidden layers, and one fully-connected output layer.^[15]
Second, AlexNet used the ReLU instead of the sigmoid as its activation function.^[15]
Besides, AlexNet changed the sigmoid activation function to a simpler ReLU activation function.^[15]
AlexNet controls the model complexity of the fully-connected layer by dropout (Section 4.6), while LeNet only uses weight decay.^[15]
The architecture used in the 2012 paper is popularly called AlexNet after the first author Alex Krizhevsky.^[16]
As mentioned above, AlexNet was the winning entry in ILSVRC 2012.^[16]
Random crops of size 227×227 were generated from inside the 256×256 images to feed the first layer of AlexNet.^[16]
An important feature of the AlexNet is the use of ReLU(Rectified Linear Unit) Nonlinearity.^[16]
The authors of AlexNet extracted random crops of size 227×227 from inside the 256×256 image boundary to use as the network’s inputs.^[16]
This can be understood from AlexNet, where FC layers contain approx.^[17]
AlexNet is a work of supervised learning and got very good results.^[18]
AlexNet was used as the basic transfer learning model.^[19]
We used AlexNet as the basic transfer learning model and tested different transfer configurations.^[19]
Original AlexNet was performed on two graphical processing units (GPUs).^[19]
Nowadays, researchers tend to use only one GPU to implement AlexNet.^[19]
Figure 2 illustrates the structure of AlexNet.^[19]
The details of learnable weights and biases of AlexNet are shown in Table 3.^[19]
Compared to traditional neural networks, there are several advanced techniques used in AlexNet.^[19]
Hence, we could not directly apply AlexNet as the feature extractor.^[19]
The AlexNet consists of five conv layers (CL1, CL2, CL3, CL4, and CL5) and three fully-connected layers (FCL6, FL7, FL8).^[19]
AlexNet can make full use of all its parameters with a big dataset.^[19]

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