tensorflow object detection: using more feature extractors with faster RCNN

Question

I'm trying to perform object detection on a custom, relatively easy dataset (with ~30k samples). I've successfully used Faster_RCNN with Resnet101_v1 (final mAP 0.9) and inception_resnet_v2 feature extractors (training in progress). Now I would like my model to run faster but still keep good performance, so I'd like to compare the ones I have, with SSD running with various versions of mobile_net. However, to know which changes in performance come from SSD and which come from the feature extractor, I'd like to also try Faster-RCNN with mobile_nets. It's also possible that this yields the tradeoff I need between performance and inference time (faster RCNN being good and slow, and mobile_nets fast).

The original MobileNets paper mentions using it with Faster RCNN, and I guess they used the tensorflow model detection API, so maybe they've released the files to adapt MobileNets to Faster RCNN ?

How can I make mobile_nets compatible with Faster-RCNN?

Answer 1

In a nutshell, a MobileNet version of the Faster-RCNN Feature Extractor will need to be created. This is something we are looking at adding, but is not a current priority.

Answer 2

I am not an expert apparently, but as far as know, you cann't use mobilenets with faster_rcnn, mobilenets is based on yolo which is a different architecture from faster_rcnn.

Answer 3

Google released its Object Detection Model recently.

https://github.com/tensorflow/models/tree/master/object_detection

You can replace feature extractor easily with this API (Xception, Inception ResNet, DenseNet, or Mobile Net) with a current object detector.

There are two common parts in many Object Recognition Systems. The first part is feature extractor (extracting features such as edges, lines, colors from image input). The second part is Object Detector (Faster R-CNN, SSD, YOLOv2).