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yolov7_plate's Issues

export onnx模型时 选择 exportnms后出现了这个错误 NYI:Named tensors are not supported with the tracer, 可能是在调用models/export.py 的第80行导致的,nn.Squential不支持命名向量,请问目前有什么好的解决办法吗?

export onnx模型时出现了这个错误 NYI:Named tensors are not supported with the tracer, 可能是在调用models/export.py 的第80行导致的,nn.Squential不支持命名向量,请问目前有什么好的解决办法吗?

Originally posted by @miracletrespasser in #1 (comment)

yolov7导出onnx模型

感谢作者提供的车牌检测的代码,请问作者能不能分享一下用yolv7实现的车牌检测转成onnx模型,我尝试着使用您在yolov5实现车牌检测中,将权重转成onnx,但在进行onnxruntime推理时,出现以下索引错误,发生异常: IndexError
index 4 is out of bounds for axis 1 with size 3

labelling

嗨,很抱歉我问这个问题,我不是来自**,但我对你为我的大学研究所做的工作很感兴趣。 您使用什么工具来标记图像? labelimg 似乎没有提供标记 4 个关键点的方法。 对不起中文不好,我用谷歌翻译

动态batch推理

你好,博主,很感谢你的项目代码,目前在用这个代码的时候遇到一点问题,我在导出onnx的时候,设置了动态batch,基于yolov5和yolov7的版本,都做了尝试,发现报了同样的错误,在多张图片同时进行推理的时候
Reshape node. Name:'/model.1/Reshape' Status Message: D:\a_work\1\s\onnxruntime\core\providers\cpu\tensor\reshape_helper.h:41 onnxruntime::ReshapeHelper::ReshapeHelper gsl::narrow_cast<int64_t>(input_shape.Size()) == size was false. The input tensor cannot be reshaped to the requested shape. Input shape:{2,96,80,80}, requested shape:{1,2,48,80,80}
看着像是在某个节点reshape的时候报错了,yolov5和v7都是在同样的节点,想问下博主这种情况怎么解决?还是我操作的有问题?

部署的时候会考虑量化模型么?

首先感谢大佬无私的开源, 整套工具都很全, 从训练到部署, 支持的也比较全, 但是我看没有做8bits量化,大佬请问是有尝试过么, 做量化会损失精度么?

IndexError: index 13 is out of bounds for axis 0 with size 4

hello! 为什莫会出现这样的错误?我的是3分类,为什莫index会出现13。
Traceback (most recent call last):
File "test.py", line 397, in
test(opt.data,
File "test.py", line 236, in test
confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))
File "/root/yolo/yolov7_plate/utils/metrics.py", line 148, in process_batch
self.matrix[detection_classes[m1[j]], gc] += 1 # correct
IndexError: index 13 is out of bounds for axis 0 with size 4

结果绘制问题

非常感谢您的代码,在训练后看结果,突然发现验证集的loss一直在增长,但是看result数据后3列是正常的,发现result中存在如下的结果:
0/299 2.58G 0.04211 0.003418 0.0004832 0.006365 0.0006483 0.05303 551 320 0.9413 0.961 0.976 0.7626 0.02045 0.001281 0.0002736
理解依次是训练周期,占用大小,训练集回归loss,置信度loss,clsloss,然后4个不太清楚意思(应该有total,target,另外两个是什么呢?),图片大小,最后是P,R,map0.5,map0.5-0.95,验证集的回归,置信度,clsloss,这样数字比之前设定的多,和画图那边的[2, 3, 4, 8, 9, 12, 13, 14, 10, 11]不匹配。

training

Sorry if i asked so much, but i experienced this error and i cant see anyone solve this in internet. thank you
image

i used pytorch 1.13.1, cuda 11.6

一个想法

设计一个车牌检测网络,同时设计出具有分类和回归的能力。回归能力已经具备了!

是否可以设计出具有多分类的能力?

比如对车牌单双层分类,对车牌颜色分类,对车牌属于车头车尾分类?而不是在检测时枚举出所有可能的类别,单层_蓝牌_车头_车牌、双层_黄牌_车尾_车牌等等

模型onnx导出问题

非常感谢大佬杰出的工作!!!我在用export.py导出onnx模型的时候,想把cat部分去掉,也看到了这一部分的开关:
image
但是这个开关只对yolov7-lite-s.pt模型有效,对yolov7-lite-t.pt模型是无效的,想请教大佬怎样才能把yolov7-lite-t.pt顺利导出呢。

tensorrt 推理编译错误 CMakeFiles/onnx2trt.dir/onnx2trt.cpp.o: In function `cvflann::anyimpl::big_any_policy<cv::String>::static_delete(void**)': onnx2trt.cpp:(.text._ZN7cvflann7anyimpl14big_any_policyIN2cv6StringEE13static_deleteEPPv[_ZN7cvflann7anyimpl14big_any_policyIN2cv6StringEE13static_deleteEPPv]+0x15): undefined reference to `cv::String::deallocate()'

如何错误,我换了4.5.2 和 3.2都不行,查了一下opencv 3.1.0-dev才有`cvflann::anyimpl::big_any_policycv::String这个函数

请问你编译的opencv版本是多少

有e部署的问题

请问有尝试过yolov_plate部署在NNIE平台的芯片上吗?我在使用onnx转caffe的时候会出现这个问题?能不能帮忙看下
image

黑色和蓝色分不清

有95%的概率将蓝色识别为黑色,白天的情况下,用视频和图片都会出现这个情况

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