How it works

For a long time, object detection models used to separate mechanisms to perform localization (where) and classification (what). These models are called two-stage detectors and still yield competitive results. However, recent work has combined these two steps into a single deep learning model, making them one-stage detectors:

These models are incredibly fast and can run at impressive frame rates even on mobile devices like an iPhone. The overall model is similar to an image classifier (see How it works). The main difference is that the network is instructed to predict the presence of multiple objects per image, each object instance associated with a bounding box localization. In fact, our model predicts a fixed set of 2535 instances. The exact number comes from 13x13x15, where 13-by-13 describes a fixed grid of center locations. The last number represents a pre-defined list of 15 canonical bounding box shapes (e.g. 32-by-32 and 256-by-128). Since most images have only a handful of object instances, the vast majority of the 2535 instances are eliminated either by having low confidence or by the non-maximum suppression algorithm (see Advanced Usage). The list of locations and shapes is meant to provide anchor boxes that should roughly cover the prediction needs of any image. In other words, given an image of an object, at least one of the 2535 anchor boxes should be reasonably close to the correct bounding box for that object. However, a perfect match will be rare. To address this, adjustment values for both location and shape are also predicted and used to tweak the fixed anchor boxes to yield more precise localization.

Transfer Learning

During training, similar to the image classifier, we employ Transfer Learning. In fact, our starting point is still an image classifier trained on 1000 classes. This means that the network has seen millions of images before it even looked at any of our data. This is great, because it reduces the data annotation burden on us and is exactly what allows us to create reasonable detectors sometimes with only 30 samples per class in our training data. However, since the starting network was not primed for detection, we do need to adapt it for the new task. This requires what is called end-to-end fine-tuning, which is the process of gently updating all the weights (parameters) for the new task, without forgetting all the useful visual semantics it previously learned. Contrast this with image classification, where it was enough to adjust the top layer. As a result, model creation time is longer for the object detector than what it is for the image classifier.


The model that we use is a re-implementation of TinyYOLO (YOLOv2 with a Darknet base network).

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