loss = torch.mean(loss)elifreduction =='sum': loss = torch.sum(loss)else:raiseNotImplementedErrorprint("last_loss:\n",loss)returnlossif__name__ =="__main__": pred_box = torch.tensor([[2,4,6,8],[5,9,13,12]]) gt_box = torch.tensor([[3,4,7,9]]) loss = Iou_loss(preds...
这样CIOU_Loss就将目标框回归函数应该考虑三个重要几何因素:重叠面积、中心点距离,长宽比全都考虑进去了。 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...
第二步;在utils/loss.py中,找到ComputeLoss类中的__call__()函数,把Regression loss中计算iou的代码,换成下面这句: iou=bbox_iou(pbox.T,tbox[i],x1y1x2y2=False,CIoU=False,EIoU=True)# iou(prediction, target) 2.alpha IoU更换方式 第一步;直接将utils/metrics.py文件中bbox_iou()替换,随后将b...
替换Inner SIoU损失函数(基于MMYOLO) 由于MMYOLO中没有实现Inner SIoU损失函数,所以需要在mmyolo/models/iou_loss.py中添加Inner SIoU的计算和对应的iou_mode,修改完以后在终端运行 python setup.py install 再在配置文件中进行修改即可。修改例子如下: elif iou_mode == "innersiou": ratio=1.0 w1_, h1_,...
(b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared 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 ...
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Cluster-NMS into YOLOv3 Pytorch Our paper is accepted byIEEE Transactions on Cybernetics (TCYB). This is the code for our paper: Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression Enhancing Geometric Factors into Model Learning and Inference for Object Detection and Instance...
Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone cd yolov5 pip install -r requirements.txt # install Environments YOLOv5 may be run in any of the following up-to-date verified environments (...
Using the PyTorch framework, this platform efficiently manages the entire process, including data loading, gradient computation, and model optimization. Table 1. Experimental platform device. Leveraging the platform’s powerful performance, this study could rapidly iterate the model for complex multi-...
compute_loss: 可选参数,用于计算损失值。 该函数主要步骤包括: 如果提供了模型,则直接使用该模型;否则根据权重文件(weights)和设备(device)加载模型; 根据任务类型(task)选择对应的数据集(data),并生成数据加载器(dataloader); 定义一个矩阵results用于存储检测结果,并将其转换成PyTorch Tensor格式; ...