Some questions

Hi @dbolya, I have some questions about the training and validation phase. I would appreciate if you can help me!

1. If I want to eval the pretrained model on a full-HD video or HD webcam, is it necessary to train the model with bigger max_size? if yes, suppose my video is 1080p what should be the max_size for training?

2. Is it possible to turn off the boundary box in eval mode and get only masks without bb and detection accuracy?

3. any suggestion on how can I modify the code to get one color per instance instead of having different colors for one instance in an image?.

Thanks!

[dbolya]

@Auth0rM0rgan I don’t know of any. Though you might be able to use some traditional CV methods to find dark blobs close to detected masks? Idk how that would work but if you don’t want to make a dataset of your own, that’s probably your best bet.

As for making masks more precise, YOLACT++'s base model gives the highest performance of all published real-time methods, but if you’re okay with reducing speed you have options:

1. You can add an extra upsample layer for higher quality masks. To do that, change the [(None, -2, {}), (256, 3, {'padding': 1})] on this line: https://github.com/dbolya/yolact/blob/db81124874817895db69f2dc443f5c24e0e3f491/data/config.py#L691 to [(None, -2, {}), (256, 3, {'padding': 1})] * 2 where the * 2 at the end dictates how much higher resolution you want (*1 for 138x138, *2 for 276x276, *3 for 552x552, etc.) though be wary of how much GPU memory you have.
2. Increase the input image size (currently 550 for the base version). Check how yolact_im700_config is defined and you can define a similar one for YOLACT++.

[dbolya]

Yeah, the process should be pretty similar as what’s in the script. The main difference is that looks like the masks are all independent, so you can just load the mask with pillow or something and convert it to numpy. Then the box is pretty straightforward, though it looks like those are relative coordinates and COCO expects absolute coordinates so you’ll have to multiply them by the image width / height.