Efficient Zero-Padding in Small Object Detection: Average Pooling vs. Strided Convolution

To ensure optimal handling of zero-padding strategies in small object prediction tasks such as traffic sign detection, we have chosen to use an average pooling layer with a 3' x 3' kernel size in the transition block instead of a strided convolution. This approach avoids suboptimal handling of zero-padding and contributes to improved accuracy and efficiency in the model.