ResNet50 Architecture - ARUNIMO/plant-disease-cnn GitHub Wiki

A more technical page dedicated to explaining how ResNet50 works in your application.

ResNet50 Architecture in Plant Vision ResNet50 is a deep convolutional neural network designed to handle image classification tasks. It uses residual blocks to avoid the vanishing gradient problem, which is common in deep networks.

Residual Learning: Makes it easier to train deep networks by using skip connections. Convolutional and Pooling Layers: These layers help extract features from the input images. Fully Connected Layers: Used for the final classification of plant diseases.