How does resnet50 perform object recognition?

ResNet50, a convolutional neural network (CNN), achieves object recognition through its deep architecture and residual learning framework. The network consists of 50 weight layers and operates by passing input images through several convolutional layers, pooling layers, and fully connected layers. By using skip connections, the network enables the direct transfer of information from earlier layers to later layers, thus reducing the vanishing gradient problem. This approach enables the network to learn intricate features of objects at different scales and levels of abstraction, ultimately leading to improved recognition accuracy. Additionally, ResNet50 employs softmax activation to assign probability scores to different image classes, allowing it to predict the most likely label for an input image during object recognition tasks.

This mind map was published on 13 September 2023 and has been viewed 120 times.