So far, we have seen a scenario where we made predictions after gradually convolving and pooling the output from the previous layer. However, we know that different layers have different receptive fields to the original image. For example, the initial layers have a smaller receptive field when compared to the final layers, which have a larger receptive field. Here, we will learn how SSD leverages this phenomenon to come up with a prediction of bounding boxes for images.
The workings behind how SSD helps overcome the issue of detecting objects with different scales is as follows:
- We leverage the pre-trained VGG network and extend it with a few additional layers until we obtain a 1 x 1 block.
- Instead of leveraging only the final layer for bounding box and class predictions, we will leverage all of the last few layers to make class and bounding box predictions.
- In place of anchor boxes, we will come up with default boxes that have a specific set of scale and aspect...
