![]() ![]() Examples of single-stage object detectors are YOLO (You Only Look Once), SSD (Single Shot multi-box Detector) and DetectNet. As the name implies, these model types just run the classifier once on the input image and do all of the work in a single pass. To predict which regions are potentially interesting, the “Faster R-CNN” model uses a Region Proposal Network, which sounds impressive but is really just a bunch of layers on top of the feature extractor - hey, what did you expect? Unfortunately, even though it has “Faster” in its name, this model is still on the slow side and not really suitable for mobile devices.įor speed freaks and mobile device users, the so-called single stage detectors are very appealing. The classifier is still run on multiple image regions, but now only on regions that are at least somewhat likely to have an object in them. This is the approach taken by the popular R-CNN family of models. You need to run the classifier many, many, many times for each image.Ī slightly smarter approach is to first try and figure out which parts of the image are potential regions of interest. This definitely works, but it gives a lot of duplicate detections. To make a proper object detector, you need to encourage the different bounding box predictors to learn different things.Īn old-school approach to object detection is to divide up the input image into many smaller, partially overlapping regions of different sizes, and then run a regular image classifier on each of these regions. And chances are, these bounding boxes will not actually enclose any of the objects but all end up somewhere in the middle of the image as a compromise. Instead of finding the locations of multiple objects, such a model will predict the same bounding box multiple times. Good try, but unfortunately that doesn’t work so well in practice.Įach bounding box predictor will end up learning the same thing and, as a result, makes the same predictions. You might think that you could just add more of these output layers, or perhaps predict 8 numbers for two bounding boxes, or 12 for three bounding boxes, etc. It doesn’t work so well when there are multiple objects of interest in the image. But it was also pretty limited - this model only predicts the location for a single object. You’ve seen how easy it was to add a bounding box predictor to the model: simply add a new output layer that predicts four numbers. 16.5 Words as tokens and word embedding.15.6 Inference with sequence-to-sequence models.Section III: Natural Language Processing Section 3: 5 chapters Show chapters Hide chapters 6.5 Training the logistic regression model.6.2 Back to basics with logistic regression.5.7 Training the classifier with regularization.Getting Started with Python & Turi Create 2.9 Bonus: Using Core ML without Vision.Getting Started with Image Classification 1.4 Can mobile devices really do machine learning?.Section I: Machine Learning with Images Section 1: 10 chapters Show chapters Hide chapters ![]()
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