Modern object detectors can rarely achieve short training time, fast inference speed, and high accuracy at the same time. To strike a balance between them, we propose the Training-Time-Friendly Network (TTFNet). In this work, we start with light-head, single-stage, and anchor-free designs, enabling fast inference speed. Then, we focus on shortening training time. We notice that producing more high-quality samples plays a similar role as increasing batch size, which helps enlarge the learning rate and accelerate the training process. To this end, we introduce a novel method using Gaussian kernels to produce training samples. Moreover, we design the initiative sample weights for better information utilization. Experiments on MS COCO show that our TTFNet has great advantages in balancing training time, inference speed, and accuracy. It has reduced training time by more than seven times compared to previous real-time detectors while maintaining state-of-the-art performances. In addition, our super-fast version of TTFNet-18 and TTFNet-53 can outperform SSD300 and YOLOv3 by less than one-tenth of their training time, respectively. Code has been made available at \url{https://github.com/ZJULearning/ttfnet}.

本论文提出了一种名为TTFNet的训练时间友好型网络，可在保持具有状态-of-the-art性能的情况下，将训练时间缩短七倍以上，并且可以快速进行目标检测，方法是使用高斯核对训练样本进行编码，以实现在训练时间，推理速度和准确性之间的平衡。

面向实时物体检测的训练时友好的网络