GrumpyPenguin23 Ok, i will open a request now!

SubstantialBaldeagle49 not at the moment, but it is just a matter of implementing an apiclient call. you can open a feauture request for a demo on github, it will help make it sooner than later

GrumpyPenguin23
It seems the solution 3 is the fastest way, and i can reuse my code easily. It works now~ That's quite interesting, but the learning curve seems a bit steep for me.Do you have any material or suggestion for learning that?

SubstantialBaldeagle49
hopefully you can reuse the same code you used to render the images until now, just not inside a training loop. I would recommend against integrating with trains, but you can query the trains-server from any app, just make sure you serve it with the appropriate trains.conf and manage the security 😉 you can even manage the visualization server from within trains using trains-agent. Open source is so much fun!

Hi GrumpyPenguin23 ,
Thanks your reply.
In 2, dose that mean i should develop another tool to parse the json standalone? 3 is a good solutionAnd i think what i actually need is the solution to integrate application based server,such as plotly dash or bokeh server in web ui?

Hi SubstantialBaldeagle49 ,

certainly if you upload all the training images or even all the test images it will have a huge bandwidth/storage cost (I believe bandwidth does not matter e.g. if you are using s3 from ec2) If you need to store all the detection results (for example, QA, regression testing), you can always save the detections json as artifact and view them later in your dev environment when you need. The best option would be to only upload "control" images and "interesting" images, I can give you some ideas on what that means if you like. The short explanation is to only report images where the accuracy is anomalous or very wrong.

I found plotly dash cannot be exported to html file, so it may cannot be used here

Maybe plotly dash can help, is there is any other solution?