ResNet-18 binary classification architecture processes 224x224x3 input images through a stem block and four residual stages with progressive downsampling and channel expansion from 64 to 512 filters. Skip connections in each residual block enable gradient flow and feature reuse across the network, with 1x1 projection layers handling spatial and channel mismatches at stage transitions. A global average pooling layer reduces the final 7x7x512 feature maps to a 512-dimensional vector, which feeds into a fully connected layer producing binary class logits via softmax. This architecture balances computational efficiency with strong feature extraction, making it ideal for practitioners building production image classifiers. Fork and customize this diagram on Diagrams.so to document your own ResNet implementations or adapt it for multi-class tasks.