神经网络引入后，检测框架变得更快更准确。然而，大多数检测方法受限于少量物体。检测和训练数据上联合训练物体检测器，用有标签的检测图像来学习精确定位，同时用分类图像来增加词汇和鲁棒性。原YOLO系统上生成YOLOv2检测器；在ImageNet中超过9000类的数据和COCO的检测数据上，合并数据集和联合训练YOLO9-After the neural network is introduced, it is becoming faster

包含YOLO，YOLOv2，YOLOv3，VGG，Resnet，imagenet等多个深度学习模型。(It includes several deep learning models such as YOLO, YOLOv2, YOLOv3, VGG, Resnet, Imagenet and so on.)

PYNQ加速YOLOv2的demo,需要PYNQ平台，希望对感兴趣的开发者有用(A demo for accelerating YOLOv2 in xilinx's fpga PYNQ)