分类损失(cls_loss):该损失用于判断模型是否能够准确地识别出图像中的对象,并将其分类到正确的类别中。 置信度损失(obj_loss):该损失用于衡量模型预测的框(即包含对象的矩形)与真实框之间的差异。 边界框损失(box_loss):该损失用于衡量模型预测的边界框与真实边界框之间的差异,这有助于确保模型能够准确地定位对象...
12、results.png train/box_loss:YOLO V5使用 GIOU Loss作为bounding box的损失,Box推测为GIoU损失函数均值,越小方框越准; train/obj_loss:推测为目标检测loss均值,越小目标检测越准; train/cls_loss:推测为分类loss均值,越小分类越准;单类检测,此值为0 precision:精确率(找对的正类/所有找到的正类); recall...
:return (Precision, Recall, map@0.5, map@0.5:0.95, box_loss, obj_loss, cls_loss) """ 3.2、初始化配置1 训练时(train.py)调用:初始化模型参数、训练设备 验证时(val.py)调用:初始化设备、save_dir文件路径、make dir、加载模型、check imgsz、 加载+check data配置信息 # === 1、初始化配置1 =...
As the image shows below, there are disorders on the Y-axis of val/box_loss, val/obj_loss and val/cls_loss. And matplotlib output the following warning: Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as ...
出现原因:大的学习率lr导致获取的特征向量为 nan,而在计算 loss 时存在一个 loss_cls 是 mmdet.CrossEntropyLoss , 其最底层调用的是 torch.nn.functional.binary_cross_entropy(pred,label), 若输入的pred 中存在NaN,则会报这个错 ref: discuss.pytorch.org/t/a ...
#Loss """ 分类损失(cls_loss):该损失用于判断模型是否能够准确地识别出图像中的对象,并将其分类到正确的类别中。 置信度损失(obj_loss):该损失用于衡量模型预测的框(即包含对象的矩形)与真实框之间的差异。 边界框损失(box_loss):该损失用于衡量模型预测的边界框与真实边界框之间的差异,这有助于确保模型能够...
分类损失(cls_loss):该损失用于判断模型是否能够准确地识别出图像中的对象,并将其分类到正确的类别中。 置信度损失(obj_loss):该损失用于衡量模型预测的框(即包含对象的矩形)与真实框之间的差异。 边界框损失(box_loss):该损失用于衡量模型预测的边界框与真实边界框之间的差异,这有助于确保模型能够准确地定位对象...
What could be the potential reason why obj_loss is increasing for validation? Anything that stands out as wrong from the config? Configs # hyp-low lr0: 0.01 lrf: 0.01 momentum: 0.937 weight_decay: 0.0005 warmup_epochs: 3.0 warmup_momentum: 0.8 warmup_bias_lr: 0.1 box: 0.05 cls: 0.5...
if cls not in classes or int(difficult) == 1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), ...
preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None) # Loss if compute_loss: loss += compute_loss(train_out, targets)[1] # box, obj, cls # NMS targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels ...