Not sure if this is the right place to ask. I am trying to build a very simple model that works with my custom data set. I got my data to look like the COCO image dataset and I am testing on COCO_tiny to see if things work.
I am struggling with were the RetinaNet or SSD models with custom heads has moved to with Fastai2 (version 2.5.6 is what I see on Colab right now…)
My data is like this:
takeoff = DataBlock(blocks=(ImageBlock, BBoxBlock, BBoxLblBlock),
## first part is the boundary 4-values, second is the label
get_y = [lambda o: img2bbox[o.name], lambda o: img2bbox[o.name]],
So I want a head that will predict the 4-values for the bbox and the category. Seems simple enough to build a resnet backbone and then the correct head. From re-watching the older videos, it seems that SSD or RetinaNet should have this as a standard output. And yet, I can’t find a code snippet that does this anywhere in the basic library for Fastai2.
I have found in the
wwf (thank you @muellerzr !) library some tools that seem to get close to this with fitting a RetinaNet with FocalLoss. But event that seems to have a few steps to get things working.
I would guess that
vision_learner would build this correctly, but I don’t get the right output head.
I found some posts about IceVision, and that seems to indicate Fastai, but I can’t figure out how to connect to that and use it.
Any hints on a full working solution (with some COCO_tiny data, for example…) that can train the head to predict the bounding box + category?
Any hints/helps/repos/scripts that get most of the way there would be extremely helpful.