Trainable Bag-of-Freebies in YOLOv7

The concept of bag-of-freebies was first introduced in YOLOv4. The development of real-time object detectors in recent years is primarily geared toward designing efficient architectures. Many of these detectors are tailored for specific devices, such as edge CPUs or GPUs. For instance, some methods like MCUNet and NanoDet aim to enhance inference speed on edge CPUs, while others like YOLO-X and YOLO-R focus on optimizing the inference speed for various GPUs.

However, YOLOv7 takes a slightly different approach. While it does consider architecture optimization, a significant emphasis is placed on training process optimization. The goal is to incorporate modules and optimization techniques that might increase the training cost (in terms of computational resources and time) but to improve object detection accuracy without adding to the inference cost. In other words, they provide free improvements in accuracy without affecting the model’s speed when it’s deployed for real-time object detection.

The main idea behind the trainable bag-of-freebies is to leverage optimized modules and techniques that can strengthen the training process, ensuring a more robust and accurate model without incurring additional costs during inference.

Earlier reparameterizing model techniques

Model reparameterization is just an ensemble An ensemble refers to a machine learning technique where multiple models (often called “learners” or “base models”) are trained to solve the same problem and combined to get better results. technique.