I am working on a SSD Object Detector using fastai and pytorch
This work was inspired from fastai's course Part -2 on 2018. https://www.youtube.com/watch?v=0frKXR-2PBY&list=PLfYUBJiXbdtTttBGq-u2zeY1OTjs5e-Ia&index=3&t=0s. And the code was inspired from their courses repository on github. https://github.com/fastai/fastai/blob/master/courses/dl2/pascal-multi.ipynb
I tried to increase the grids from just 4, 2 and 1 to 28, 14, 7, 4, 2, 1. I am trying to create a SSD Model which infers classes and bounding boxes from 6 different grid sizes. In the original SSD paper they have used grid sizes of 19, 10, 7, 4, 2, 1. I have used a slightly different architecture.
I have hooked outputs of the specified grid size from a pretrained model and pass those hooked outputs thorugh a ResBlock and a Out Conv layer which provides the classification
and bounding box outputs.I also have a custom head to the pretrained model where I get inferences on grid sizes smaller than 7.
Thanks to @muellerzr 's repo https://github.com/muellerzr/Practical-Deep-Learning-for-Coders-2.0.git from where the 'imports' folder is taken from his computer vision section . This imports folder contains the RetinaNet Model and RetinaNet local Loss and other inference tools for object detection.
The Architecture image is below:
Some Additional functionality added are : 1) A Callback for recording hyper parameter changes for every layer 2) A Interactive Learning Rate Scheduler using jupyter widgets to schedule interactively
