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I use my own dataset( 3 classes) to train a vgg16_reduced model. And it works well in mxnet-ssd python. And I want to use the c++ interface. First, I use deploy.py to deploy the model. I test it in mxnet-ssd.cpp.And there is no errors , but the result is incorrect(no detection). I test the pretrained model resnet50_ssd_512_voc0712_trainval , it works well. I also trained inceptionv3 model, the result is same.
我使用自己的数据集(3类)训练了VGG16模型,在python版本下的mxnet中完全可以正常工作,现在想使用C++进行部署。首先使用deploy.py进行了模型转换,然后使用c++的ssd发现使用自己训练的模型结果不正确(检测不到但是没有任何报错),而使用给出的预训练模型时完全没有问题。我也尝试了自己训练的Inceptionv3模型 结果和自己训练的vgg16是一样的 没有报错但就是检测不到。不知道问题到底出在了哪里。。
The text was updated successfully, but these errors were encountered:
I use my own dataset( 3 classes) to train a vgg16_reduced model. And it works well in mxnet-ssd python. And I want to use the c++ interface. First, I use deploy.py to deploy the model. I test it in mxnet-ssd.cpp.And there is no errors , but the result is incorrect(no detection). I test the pretrained model resnet50_ssd_512_voc0712_trainval , it works well. I also trained inceptionv3 model, the result is same.
我使用自己的数据集(3类)训练了VGG16模型,在python版本下的mxnet中完全可以正常工作,现在想使用C++进行部署。首先使用deploy.py进行了模型转换,然后使用c++的ssd发现使用自己训练的模型结果不正确(检测不到但是没有任何报错),而使用给出的预训练模型时完全没有问题。我也尝试了自己训练的Inceptionv3模型 结果和自己训练的vgg16是一样的 没有报错但就是检测不到。不知道问题到底出在了哪里。。
The text was updated successfully, but these errors were encountered: