然后你就继续在train.py里面搜索half关键字,把所有有.half()变为.float(),如下图: 到这一步为止,我train时出现为nan和0的问题已经解决了。 3、如果发现问题依旧没有解决,还需要在val.py里面将所有的half改为False,同时im.half() if half else im.float() 改为 im.float()。如下图: 以上3步完成之后,...
【CAAI】yolov5训练出现loss为NAN。 Describe the current behavior / 问题描述 (Mandatory / 必填) 1.使用贵公司官网推荐的数据集进行训练,出现loss为NAN的情况。 Environment / 环境信息 (Mandatory / 必填) Hardware Environment(Ascend/GPU/CPU) / 硬件环境: GPU Please delete the backend not involved / 请...
结果发现,跑出来的图 什么都没有,box loss 什么的一直nan 换个电脑 重复步骤,conda还是在新环境装不了pytorch,用pip安装网速巨慢,所以直接访问终端下载地址下载,之后pip install 把文件拖到终端直接安装 装完发现依旧跑不了 报错应该和torchvision有关 : Starting training for 100 epochs... Epoch GPU_mem box_...
2、转换数据格式 接下来准备labels,把数据集格式转换成yolo_txt格式,即将每个xml标注提取bbox信息为txt格式,每个图像对应一个txt文件,文件每一行为一个目标的信息,包括class, x_center, y_center, width, height格式。格式如下: 创建voc_label.py文件,将训练集、验证集、测试集生成label标签(训练中要用到),同时...
模型的训练我们采用多尺度图片输入进行训练,来使得模型具有尺度的鲁棒性,这里要提一点,如果是通过每次输入图片的时候来随机选择尺度方式输入(即YunYang代码中的方式)来多尺度训练,训练中的loss容易出现nan,为了避免这个问题可以在每个batch之间随机选择尺度而不是每个batch内来随机选择尺度。
如需网络结构高清图和模型权重，可点击查看下载（https://blog.csdn.net/nan355655600/article/details/...
The original yolo/darknet box equations have a serious flaw. Width and Height are completely unbounded as they are simply out=exp(in), which is dangerous, as it can lead to runaway gradients, instabilities, NaN losses and ultimately a complete loss of training. ...
Objectness loss,obj损失,采用的依然是BCE loss,注意这里的obj指的是网络预测的目标边界框与GT Box的CIoU。这里计算的是所有样本的obj损失。 Location loss,定位损失,采用的是CIoU loss,注意只计算正样本的定位损失。 L o s s = λ 1 L c l s + λ 2 L o b j + λ 3 L l o c Loss=\lambda_1...
Search before asking I have searched the YOLOv5 issues and discussions and found no similar questions. Question When I use VisDrone dataset to train YOLOv5, after a few epoch, the loss becomes nan, and there is no prediction. Someone sai...
box_loss quantifies how well the model predicts the location and size of the bounding boxes around the objects. Lower box_loss means the model is better at predicting where objects are and their scale. cls_loss (classification loss) measures the accuracy of predicting the correct category of ...