How to reproduce the performence on CUHK-PEDES?

[liyingGao]

I use python 3.6.4, Pytorch1.0.0 & torchvision 0.2.1, scipy 1.2.1.
The results in paper 'Deep Cross-Modal Pojection Learning for Image-Text Matching' on CUHK-PEDES are:{top- 1 = 49.37%，top-10 = 79.27%}, but I only get {top- 1 = 38.35%，top-10 = 63.39%} using MobileNetv1 as backbone, and {top- 1 = 41.44%，top-10 = 65.66%} using Resnet152. I wander if anyone could reproduce the results, and if it is convenient, please share the training details and hypeparameters.

[npumazehong]

I use python 3.6.4, Pytorch1.0.0 & torchvision 0.2.1, scipy 1.2.1.
The results in paper 'Deep Cross-Modal Pojection Learning for Image-Text Matching' on CUHK-PEDES are:{top- 1 = 49.37%，top-10 = 79.27%}, but I only get {top- 1 = 38.35%，top-10 = 63.39%} using MobileNetv1 as backbone, and {top- 1 = 41.44%，top-10 = 65.66%} using Resnet152. I wander if anyone could reproduce the results, and if it is convenient, please share the training details and hypeparameters.
?
I have the similar results like you.Have you reproduced the paper's result using pytorch ? Can we share with each other?

[yyll1998]

I use python 3.6.4, Pytorch1.0.0 & torchvision 0.2.1, scipy 1.2.1.
The results in paper 'Deep Cross-Modal Pojection Learning for Image-Text Matching' on CUHK-PEDES are:{top- 1 = 49.37%，top-10 = 79.27%}, but I only get {top- 1 = 38.35%，top-10 = 63.39%} using MobileNetv1 as backbone, and {top- 1 = 41.44%，top-10 = 65.66%} using Resnet152. I wander if anyone could reproduce the results, and if it is convenient, please share the training details and hypeparameters.
?
I have the similar results like you.Have you reproduced the paper's result using pytorch ? Can we share with each other?

I have the same problem with you.Have you solved this problem? If you have,could we share with each other?Thank you !