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Introduction
EfficientNet is one of the most important in a wide range of Convolution Neural Networks. It outperformed all of its predecessors in terms of performance. It was created by Google AI researchers and is founded on the brilliant fusion of two essential ideas: compound scaling and Neural Architecture Search (NAS).
EfficientNet has transformed the industry by exceeding its competitors in terms of performance and resource efficiency. Its ability to find a compromise between accuracy and processing cost has established it as an important step forward in CNN (Convolutional Neural Network) development.
What is a Neural Network?
Neural networks are complex networks composed of artificial neurons that may accept several inputs and produce a single output. A Neural Network's principal function is to convert input into meaningful output. A Neural Network typically comprises an input and output layer, as well as one or more hidden layers. It is sometimes referred to as an Artificial Neural Network (ANN). The ANN design in Neural Networks behaves similarly to the human brain and is crucial.
All neurons in a Neural Network influence each other and are thus all connected. The network can recognize and observe every facet of the information at hand, as well as how the various parts of data may or may not be related to one another. This is how Neural Networks can identify incredibly complicated patterns in massive amounts of data.
Introduction to EfficientNet
EfficientNet is a Neural Network architecture that uses compound scaling to improve performance. By lowering the number of parameters and FLOPs (Floating point Operations Per Second), EfficientNet tries to enhance performance while enhancing computational efficiency.
In general, scaling up (raising the number of layers) in CNN designs is difficult because there are multiple approaches to scaling up. Manually selecting the ideal combination takes a long time. EfficientNet handles scaling up using Compound Scaling and NAS (Neural Architecture Search), as discussed later in the text.
Efficient Net is divided into two sections:
Use NAS to create an effective baseline architecture
To improve performance, use the Compound Scaling Method while scaling up
The baseline architecture is essential because the scaling-up procedure improves the baseline model's performance. As a result, the better the baseline model, the higher the ultimate scaling performance. Since the basic compound scaling method can be applied to other architectures, such as ResNet, hence baseline architecture performance is much more important.
The above architecture is formed using NAS(Neural Architecture Search). The operation of searching for the most effective Neural Network architecture with the fewest losses and the highest computational efficiency is known as NAS. This architecture employs Mobile inverted Bottleneck Convolution (MBConv), which is similar to the MobileNetV2 architecture. This baseline architecture is then compound scaled to provide a variety of EfficientNet models.
Compound Scaling
There are three ways to scale up:
Width scaling
Depth scaling
Resolution scaling
Scaling up in each of the three techniques improves performance, but after a certain point, the improvement peaks and does not continue. For example, a 100-layer network would operate similarly to a 500-layer network.
It was discovered that using a variety of methods gave better outcomes than using just one strategy. This can be understood more intuitively. The number of pixels increases as the width increases. As a result, the same number of Convolution layers cannot capture the same number of features as they could when the width was smaller. As a result, the depth should be adjusted to meet the increased width in order to capture the characteristics. Compound scaling is a way of scaling up that uses all three parameters. This is accomplished by maintaining the parameters' values balanced. So that one metric does not overtake the other, reducing performance. To maintain it balanced, we use the equation below. Depth: d = α ^ Φ
Width: w = β ^ Φ
Resolution: r = γ ^ Φ
Such that,
α. (β ^ 2). (γ ^ 2) is almost equal to 2
α >= 1, β >= 1, γ >=1
The values are stated in terms of alpha, beta, and gamma and are raised to a given power. Their output is equal to zero. We can see that the width and resolution have been increased to power 2. This refers to the variable's effect on FLOPS. Doubling the depth doubles the FLOPS value, whereas doubling the width and resolution quadruples the FLOPS value. As a result, those two parameters are increased to power 2.
NAS-Based EfficientNet Architecture
The goal of Neural Architecture Search (NAS) is to find more efficient and optimized architectures with greater performance while keeping a variety of characteristics in mind. It examines and evaluates a large number of designs in the search space to discover the best model for the specified goal.
The EfficientNet architecture is built around this NAS approach. NAS is used to discover the most powerful architecture while minimizing the number of parameters, resulting in a highly efficient and optimized model.
EfficientNet Performance
In general, the EfficientNet models outperform previous CNNs in terms of accuracy and efficiency, lowering parameter size and FLOPS by an order of magnitude. In the high-accuracy domain, for example, the EfficientNet-B7 achieves cutting-edge 84.4% top-1 / 97.1% top-5 accuracy on ImageNet while being 8.4x smaller and 6.1x quicker on CPU inference than the prior Gpipe.
Though EfficientNets perform well on ImageNet, they should also translate to other datasets to be truly helpful. To demonstrate this, EfficientNets was run on eight widely used transfer learning datasets. EfficientNets obtained state-of-the-art accuracy in 5 of the 8 datasets.
Benefits and Drawbacks of EfficientNet
Benefits:
EfficientNet outperforms many other models by achieving exceptional accuracy levels while using fewer parameters
It has a scalable architecture that is simple to modify to meet various computational needs, making it adaptable for a range of jobs
EfficientNet is more time and money efficient because of its efficient architecture, which also greatly reduces training time
In numerous computer vision applications like image classification, object recognition, and picture segmentation, EfficientNet demonstrates its value
Drawbacks:
EfficientNet's complex design might provide difficulties during initial setup and fine-tuning, necessitating specialized knowledge
EfficientNet's internal operations, like those of the majority of deep neural networks, can be difficult to comprehend and explain
To improve the model's resilience and generalizability, appropriate and comprehensive data augmentation may be necessary
Frequently Asked Questions
What is a perceptron?
A perceptron is analogous to a neuron in the human brain. It collects input from many entities and applies functions to these inputs, transforming them into output. A perceptron is mostly used for binary classification, where it perceives an input, computes functions as per the input's weights, and outputs the needed transformation.
What are activation functions?
In Deep Learning, activation functions are entities that are utilized to transform inputs into useful output parameters. It is a function that determines whether or not a neuron requires activation by computing the weighted sum with the bias.
Why is the need for Fourier transform in Deep Learning?
The Fourier transform is a powerful tool for evaluating and organizing massive amounts of data in a database. It can receive and handle real-time array data. This assures great efficiency while also making the model more adaptable to processing a wide range of information.
What is data normalization in Deep Learning?
Normalization of data is a preprocessing operation used to fit the data into a given range. As a result of the improved convergence when doing backpropagation, the network may learn more effectively.
Conclusion
EfficientNet offers higher performance and scalability with fewer parameters, and it marks the turning point in deep learning. It is a top choice for many computer vision jobs because it can achieve cutting-edge accuracy while using minimal computational resources. In this blog, we discussed what a Neural Network and EfficientNet are, their architecture, along with various benefits and drawbacks of EfficientNet.
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