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CNN | object detection and classification | Practicle guide
A Practical Guide to Selecting CNN Architectures for Computer Vision Applications
From LeNet to EfficientNet: Choosing the Best CNN Architecture for Your Project
Working in computer vision and machine learning is amazing because every few months, someone comes up with something crazy that completely changes your perspective on what is feasible.
Convolutional Neural Networks (CNNs) are a type of artificial neural network that has revolutionized the field of computer vision, particularly in image recognition and classification tasks. CNNs have numerous architecture designs that cater to different use cases and applications. There are many blogs and papers online explaining the architectures of CNN models in detail but I am directly explaining when to use particular architecture [I attached links to understand each network in detail so you can go through that also]. Here are some of the most popular CNN architectures and when to use them.
LeNet: LeNet was one of the first convolutional neural networks, and it has been around since the 1990s. This architecture is relatively simple, with only 7 layers.
When to use: It works well for small-scale image classification tasks, such as recognizing handwritten digits, but it is not suitable for more complex tasks that require a deeper network.
AlexNet: AlexNet was the first CNN to win the ImageNet Large Scale Visual Recognition Challenge in 2012, which marked a breakthrough in the field of computer vision. AlexNet is a deep CNN with 8 layers.
When to use: It works well for large-scale image classification tasks. It is particularly suitable for tasks that require a high degree of accuracy and a large dataset.
