ResNet50 For Detecting Fundus Eye Diseases

• Machine Learning, Architecture
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ResNet50 is a convolutional neural network (CNN) that stands out for its depth, comprising 50 layers, and its innovative use of residual learning. Developed by Microsoft Research, it was introduced in 2015 and quickly became a benchmark for visual recognition tasks due to its high accuracy and efficiency.

The "ResNet" in ResNet50 stands for Residual Network. Its core idea is to utilize shortcut connections, also known as identity connections, which allow the network to skip one or more layers. These connections help to address the vanishing gradient problem that often occurs in deeper networks, enabling the training of much deeper networks without a degradation in performance.

This is why ResNet50 is widely used; it can effectively learn from a vast amount of data and recognize a wide range of images with high accuracy.

The importance of ResNet50 lies in its versatility and performance. It has been successfully applied to a variety of tasks, from image classification to object detection, and has won competitions such as the ILSVRC 2015 classification competition. Its architecture is less complex and requires fewer filters compared to other deep networks like VGGNets, which contributes to its efficiency.