YOLOv5｜YOLOv7｜YOLOv8改各种IoU损失函数：YOLOv8涨点Trick，改进添加SIoU损失函数、EIoU损失函数、GIoU损失函数、α-IoU损失函数

YOLOv5｜YOLOv7｜YOLOv8改各种IoU损失函数：YOLOv8涨点Trick，改进添加SIoU损失函数、EIoU损失函数、GIoU损失函数、α-IoU损失函数

推荐整理分享YOLOv5｜YOLOv7｜YOLOv8改各种IoU损失函数：YOLOv8涨点Trick，改进添加SIoU损失函数、EIoU损失函数、GIoU损失函数、α-IoU损失函数，希望有所帮助，仅作参考，欢迎阅读内容。

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💡本篇文章 基于 YOLOv5、YOLOv7芒果改进YOLO系列：YOLOv7改进IoU损失函数：YOLOv7涨点Trick，改进添加SIoU损失函数、EIoU损失函数、GIoU损失函数、α-IoU损失函数、打造全新YOLOv7检测器。

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文章目录解析｜YOLOv7网络模型源代码训练推理教程解析总结｜YOLO系列期刊创新点总结核心代码改进改进核心代码改进α-IoU核心代码SIoU改进EIoU改进GIoU改进α-IoU改进代码直接运行解析｜YOLOv7网络模型源代码训练推理教程解析手把手调参最新 YOLOv7 模型 推理部分（一）🌟手把手调参最新 YOLOv7 模型 训练部分（二）🌟总结｜YOLO系列期刊创新点总结

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核心代码改进

以下SIoU、EIoU、GIoU、α-IoU改进，代码均在博主开源的YOLOAir中有写

改进核心代码

在YOLOv5中，使用以下函数替换原有的utils/metrics.py文件中的bbox_iou函数

如果在YOLOv7中，使用以下函数替换原有的utils/general.py文件中的bbox_iou函数

def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, EIoU=False, SIoU=False, eps=1e-7): # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 box2 = box2.T # Get the coordinates of bounding boxes if x1y1x2y2: # x1, y1, x2, y2 = box1 b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] else: # transform from xywh to xyxy b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 # Intersection area inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) # Union Area w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps union = w1 * h1 + w2 * h2 - inter + eps iou = inter / union if CIoU or DIoU or GIoU or EIoU or SIoU: cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height if CIoU or DIoU or EIoU or SIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared if DIoU: #DIoU return iou - rho2 / c2 # DIoU elif CIoU: #CIoU https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) with torch.no_grad(): alpha = v / (v - iou + (1 + eps)) return iou - (rho2 / c2 + v * alpha) # CIoU elif SIoU:# SIoU s_cw = (b2_x1 + b2_x2 - b1_x1 - b1_x2) * 0.5 s_ch = (b2_y1 + b2_y2 - b1_y1 - b1_y2) * 0.5 sigma = torch.pow(s_cw ** 2 + s_ch ** 2, 0.5) sin_alpha_1 = torch.abs(s_cw) / sigma sin_alpha_2 = torch.abs(s_ch) / sigma threshold = pow(2, 0.5) / 2 sin_alpha = torch.where(sin_alpha_1 > threshold, sin_alpha_2, sin_alpha_1) angle_cost = torch.cos(torch.arcsin(sin_alpha) * 2 - math.pi / 2) rho_x = (s_cw / cw) ** 2 rho_y = (s_ch / ch) ** 2 gamma = angle_cost - 2 distance_cost = 2 - torch.exp(gamma * rho_x) - torch.exp(gamma * rho_y) omiga_w = torch.abs(w1 - w2) / torch.max(w1, w2) omiga_h = torch.abs(h1 - h2) / torch.max(h1, h2) shape_cost = torch.pow(1 - torch.exp(-1 * omiga_w), 4) + torch.pow(1 - torch.exp(-1 * omiga_h), 4) return iou - 0.5 * (distance_cost + shape_cost) else:# EIoU w_dis=torch.pow(b1_x2-b1_x1-b2_x2+b2_x1, 2) h_dis=torch.pow(b1_y2-b1_y1-b2_y2+b2_y1, 2) cw2=torch.pow(cw , 2)+eps ch2=torch.pow(ch , 2)+eps return iou-(rho2/c2+w_dis/cw2+h_dis/ch2) else: c_area = cw * ch + eps # convex area return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf return iou # IoU改进α-IoU核心代码def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9): # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4 box2 = box2.T # Get the coordinates of bounding boxes if x1y1x2y2: # x1, y1, x2, y2 = box1 b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] else: # transform from xywh to xyxy b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 # Intersection area inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) # Union Area w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps union = w1 * h1 + w2 * h2 - inter + eps # change iou into pow(iou+eps) # iou = inter / union iou = torch.pow(inter/union + eps, alpha) # beta = 2 * alpha if GIoU or DIoU or CIoU: cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 c2 = (cw ** 2 + ch ** 2) ** alpha + eps # convex diagonal rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2) rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2) rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha # center distance if DIoU: return iou - rho2 / c2 # DIoU elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) with torch.no_grad(): alpha_ciou = v / ((1 + eps) - inter / union + v) # return iou - (rho2 / c2 + v * alpha_ciou) # CIoU return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha)) # CIoU else: # GIoU https://arxiv.org/pdf/1902.09630.pdf # c_area = cw * ch + eps # convex area # return iou - (c_area - union) / c_area # GIoU c_area = torch.max(cw * ch + eps, union) # convex area return iou - torch.pow((c_area - union) / c_area + eps, alpha) # GIoU else: return iou # torch.log(iou+eps) or iouSIoU改进

参考上面的核心代码

将iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)替换为iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, SIoU=True)EIoU改进

参考上面的核心代码

将iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)替换为iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, EIoU=True)GIoU改进

参考上面的核心代码

将iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)替换为iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, GIoU=True)α-IoU改进

参考上面的核心代码

bbox_alpha_iou将iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)替换为iou = bbox_alpha_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)

以上是yolov5的改进

yolov7 将 tbox[i] 改为 selected_tbox

比如 iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) 改为iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True)

代码直接运行python train.py cfg yolov7.yaml即可