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Trainable Bag-of-Freebies in YOLOv7

Trainable Bag-of-Freebies in YOLOv7

Learn how the trainable bag-of-freebies improved model performance.

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 ...

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