YOLO-Former: YOLO Shakes Hand With ViT
This work addresses object detection for computer vision applications, presenting an incremental improvement through hybrid integration.
The paper tackles object detection by integrating transformer architecture with YOLOv4, achieving a mean average precision of 85.76% on Pascal VOC while maintaining a frame rate of 10.85 fps.
The proposed YOLO-Former method seamlessly integrates the ideas of transformer and YOLOv4 to create a highly accurate and efficient object detection system. The method leverages the fast inference speed of YOLOv4 and incorporates the advantages of the transformer architecture through the integration of convolutional attention and transformer modules. The results demonstrate the effectiveness of the proposed approach, with a mean average precision (mAP) of 85.76\% on the Pascal VOC dataset, while maintaining high prediction speed with a frame rate of 10.85 frames per second. The contribution of this work lies in the demonstration of how the innovative combination of these two state-of-the-art techniques can lead to further improvements in the field of object detection.