How does model object recognition work using ResNet50?

ResNet50 is a convolutional neural network architecture that is widely used for object recognition tasks. It employs a deep learning approach to recognize and categorize objects in an image. ResNet50 utilizes residual blocks, which are specialized layers designed to address the vanishing gradient problem in deep networks. These residual blocks allow for the training of deeper neural networks, leading to better accuracy in object recognition. The model works by passing an input image through a series of convolutional layers, pooling layers, and fully connected layers. These layers extract features from the image and learn patterns that are characteristic of different objects. The final layers of the network classify the extracted features into different object categories. With its deep architecture and residual blocks, ResNet50 has achieved state-of-the-art performance in object recognition tasks.

This mind map was published on 20 September 2023 and has been viewed 147 times.