VanillaNet
VanillaNet presents a minimalist approach to neural networks, enhancing efficiency without sacrificing performance. Its architecture reduces complexity by eliminating layers, shortcuts, and attention mechanisms, which results in faster inference speeds. Achieving 81% Top-1 accuracy with 3.59ms latency on 11 layers, VanillaNet outperforms models like ResNet-50 and Swin-S. This approach redefines deep learning models with its optimal balance of speed, accuracy, and simplicity in tasks like detection and segmentation.
