Improving Tiny YOLO with Fewer Model Parameters

With the rapid development of convolutional neural networks (CNNs), there are a variety of techniques that can improve existing CNN models, including attention mechanisms, activation functions, and data augmentation. However, integrating these techniques can lead to a significant increase in the number of parameters and FLOPs. Here, we integrated Efficient Channel Attention Net(ECA-Net), Mish activation function, All Convolutional Net (ALL-CNN), and a twin detection head architecture into YOLOv4-tiny, yielding an AP
50
of 44.2% on the MS COCO 2017 dataset. The proposed Attention ALL-CNN Twin Head YOLO (A
²
-YOLO) outperforms the original YOLOv4-tiny on the same dataset by 3.3% and reduces the model parameters by 7.26%. Source code is at https://github.com/e96031413/AA-YOLO