Deepstream Custom model with white background not complying with coloured and textured backgrounds


I got stuck with a problem, Thanks in advance for the solution.

The custom model is trained to detect person and faces with data set of the respective images in a White Background.

The model got an accuracy about 98% for both classes.

While checking the model accuracy in deep stream with coloured and textured background, the inferred bounding boxes seem to populate randomly all-over the screen.

My Questions :

  1. Does the quality of the data set plays a vital role ?
    [Such as sharpness/blurred edges, textures, colours]

  2. If the model is trained in a data set of simple background to detect object, will the performance of the get worse on inferring over complex background ?

There is no update from you for a period, assuming this is not an issue any more.
Hence we are closing this topic. If need further support, please open a new one.

  1. pls share your setup with us
  2. You can use the trtexec to evaluate the accuracy for your model, then you can deploy it to DS if the accuracy meet your targets.