This week, we're still trying to see if the network is really learning something about the vehicles in our images.
We cropped one image with a big white truck:
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We then ran the heatmap on this, against a different image in the same class (so it's LITERALLY the same truck).
Our hypothesis was that the ONLY possible thing in the image that could be similar between the two would be the trucks.
We ran this test a couple times, moving the cropped truck around and here were our results:
You can see... not great.
Some of our theories on why the model might not be so great at tracking cars is that it really only needs to pay attention to some things in the scene, not necessarily every single vehicle.
We're also thinking that, because our classes have frames that are very close together, the model always has a nearly identical image to look at. If we skip more frames between images in our classes, this could help this problem.
Our plans for next week are to:
- Spread out the frames within our classes, so the model will have to keep track of cars over longer distances/ won't have another image that looks nearly identical
- Get new data, with less traffic jams
- Create long video of the highway