目标检测算法终结篇：YOLOv7（非正式版）正式开源-技术圈

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从此不迷路

人工智能与算法学习

转自：计算机视觉研究院

作者：Edison_G

请注意，这里的7，不是下一代YOLO，而是一个幸运数字，姑且可以看作是一个代号。它的目的是让YOLO全面开花，不仅仅只是做目标检测。也不是简单的加一个semantic head做分割，而是做一个体系的目标检测积木模块，即插即用，使之能够更简单的做复杂的上层任务，比如多个分类head，实例分割，甚至是加上姿态检测等等。

转自《知乎——金天》

链接：https://zhuanlan.zhihu.com/p/464007111

介绍

目前支持的东西就这么一些，现列举一些大家可能感兴趣的：

支持GridMask，Mosiac的数据增强，而且可以可视化；
支持YOLOX（很强），而且部署方式比原版更方便，可训练，官方不是唯一可选；
支持YOLOv7+实例分割，是SingleStage的方式，目前用的是OrienMaskHead，未来可能增加更多方式；
支持DETR，AnchorDETR等transformer，这是独一无二的，在一个框架里面同时被支持；
YOLOv4 contained with CSP-Darknet53;
YOLOv7 arch with resnets backbone;
YOLOv7 arch with resnet-vd backbone (likely as PP-YOLO), deformable conv, Mish etc;
GridMask augmentation from PP-YOLO included;
YOLOv7 arch Swin-Transformer support (higher accuracy but lower speed);
YOLOv7 arch Efficientnet + BiFPN;
YOLOv5 style positive samples selection, new coordinates coding style;
RandomColorDistortion, RandomExpand, RandomCrop, RandomFlip;
CIoU loss (DIoU, GIoU) and label smoothing (from YOLOv5 & YOLOv4);
YOLOF also included;
YOLOv7 Res2net + FPN supported;
Pyramid Vision Transformer v2 (PVTv2) supported;
WBF (Weighted Box Fusion), this works better than NMS, link;
YOLOX like head design and anchor design, also training support;
YOLOX s,m,l backbone and PAFPN added, we have a new combination of YOLOX backbone and pafpn;
YOLOv7 with Res2Net-v1d backbone, we found res2net-v1d have a better accuracy then darknet53;
Added PPYOLOv2 PAN neck with SPP and dropblock;
YOLOX arch added, now you can train YOLOX model (anchor free yolo) as well;
DETR: transformer based detection model and onnx export supported, as well as TensorRT acceleration;
AnchorDETR: Faster converge version of detr, now supported!