Image detections on mobile devices

2019/05/28

I have been working on image detection on mobile devices for the last 6 weeks, which is not long enough to delve into some research paper on model compression or pruning techniques but adequate to get into and conduct some experiments on some detection models.


Firstly, let look at the detection model and make some terms. A detection usually (and from some of the state-of-the-art model: RetinaNet) is composed of 3 components, from bottom to top:

  1. Backbone: feature extractions.
  2. Region Proposal Module: produce proposals based on feature maps of the backbone and feed them into the last component.
  3. Head predictor: Predict the bounding boxes and label.

In this note, I will talk about all 3 components when we develop an detection model for mobile devices. Let assume that we are working on Android devices and the deep learning frameworks on mobiles support some common layers, but it is not identical to any other frameworks such as Tensorflow, Pytorch. Therefore, we also have to deal with one more step, namely converting the model trained from the workstation/servers into the model for mobile devices.

Model Capacity

Backbone

Here is the list of backbone I have been trying:

Some observations

Implementations

Converting models

Conclusions and Takeaways

  1. If you have to deal with training dataset, make sure that the groundtruths, annotations are consistent and correct because small networks do not have large capacity to learn from label noise.
  2. Lightweight networks behave very different between classification and detection tasks. Make sure that you have proper experiments to justify the model.
  3. GroupNorm is good for detection, use it if it is available.
  4. Sometimes, the FPN and head predictor are pretty heavy compared to the backbone. Some tricks to reduce memory and improve the inference time are:
    • Remove certain octave scales, especially FPN 3.
    • Replace normal Conv layers in FPN branches with Depthwise layer.
    • Reduce anchor scales, aspect ratios or even number of classes if necessary.
  5. In general, Depthwise conv is a good choice to replace the normal conv layer. However, the performance depends on the underlying implementations. You may not gain any improvements because of it.
  6. Different frameworks uses different settings, implementations even with some de facto layers. While converting the model, make sure that the logic is correct. You may have to retrain the model to the final settings based on the mobile framework.

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