search

duanzhiihao / RAPiD

RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images (CVPR 2020 Workshops)
http://vip.bu.edu/rapid/
Other
212 stars 63 forks source link

Tiny RAPiD on embedded devices #22

Open anderflash opened 3 years ago

anderflash commented 3 years ago

Hi!

Do you plan to provide a tiny version of the network with, for instance, MobileNetv3 as backbone which can be run even on embedded devices?

If not, do you have any hints about how to change parts of the code to make it faster?

Thanks!

duanzhiihao commented 3 years ago

Hi, thank you for your interest.

We do have such a plan last year when we were trying to use "YOLOv5" as the detector instead of YOLOv3. But after some experiments, we find that it didn't give a better complexity-accuracy trade-off, so we gave up that idea. The reason might be that our training setting is sub-optimal because we do not have enough GPUs to tune the hyperparameters.

If you would like to customize your own tiny version RAPiD, I suggest replacing the "backbone" and the "Feature Pyramid Network" because they are the most computation-expensive parts: https://github.com/duanzhiihao/RAPiD/blob/e56ac87b0422d98f2942dbfbc64b745a3a3149ae/models/rapid.py#L27 https://github.com/duanzhiihao/RAPiD/blob/e56ac87b0422d98f2942dbfbc64b745a3a3149ae/models/rapid.py#L54