About Precision Reproduction

[PX-Xu]

Dear authors:
I really appreciate your work. But there are some problems when I reproduce your work.
Firstly, I used the weight that you provided in the Google driver. The result is below:

It seems like the mAP is lower than the number in your paper. The mAP in the paper is 48.9%. And I use the weight to reproduce the result. The mAP is 46.6%.

Furthermore, I follow the instructions in this repository to train and reproduce this work in foggy-cityscapes dataset. The result is below:

There are large gaps between the mAP in your paper and the reproduced result.

I wonder is any problem with my val dataset. Or are there any other settings when training?

Hope you respond!

Best wishes!

[github-actions]

👋 Hello @PX-Xu, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[qinhongda8]

Dear authors: I really appreciate your work. But there are some problems when I reproduce your work. Firstly, I used the weight that you provided in the Google driver. The result is below:

It seems like the mAP is lower than the number in your paper. The mAP in the paper is 48.9%. And I use the weight to reproduce the result. The mAP is 46.6%.

Furthermore, I follow the instructions in this repository to train and reproduce this work in foggy-cityscapes dataset. The result is below:

There are large gaps between the mAP in your paper and the reproduced result.

I wonder is any problem with my val dataset. Or are there any other settings when training?

Hope you respond!

Best wishes!

由于这个方法包含3个点，建议你复现出现问题的话，可以通过消融实验的方式来判断是哪一个部分出现了问题，可以按照我们论文中的实验流程，分别按源域训练、对抗部分和图像转换步骤来进行，并根据每次的mAP结果来分析。另外，这份代码为完整代码。

[PX-Xu]

Thanks for your response! I will follow your advice to try it.

[PX-Xu]

你好，能否提供QTnet训练的权重呢？我一直无法复现结果。