DSFD stopping without error about dsfd-pytorch-inference

[hukkelas]

Could it be the program running out of memory?

If you are using different shapes for each image, there could be an increase in memory consumption per image.
We are caching the priors for each image shape (see here).

You could try to disable the caching, however, this will increase the runtime quite a bit.

[QuentinSpalla]

Thank you for you quick answer.

Could it be the program running out of memory?

Yes maybe it could be possible but why RetinaNetRestNet50 is not running out of memory on the same images ?
Both detectors do not save in cache the images ?

You could try to disable the caching, however, this will increase the runtime quite a bit.

Ok thank you for the advice it I want to disable the caching I juste have to comment the line you sent ?
And you are saying that it will be slower, but do you know approximately in what proportions ?

[hukkelas]

Yes maybe it could be possible but why RetinaNetRestNet50 is not running out of memory on the same images?

That's true. We're caching the priors in RetinaNet somewhat different, therefore, it might not run into the same issue.

Ok thank you for the advice it I want to disable the caching I juste have to comment the line you sent ?

Yep!

And you are saying that it will be slower, but do you know approximately in what proportions ?

This is highly dependent on the dataset you have. If all images has a different shape, then it should be equally fast.

Another point is that it might be that you're running out of memory on your GPU. You could limit the max resolution to (for example) 1080.