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What is the time complexity? The time complexity of an algorithm is the number of basic operations, such as multiplications and summations, that the algorithm performs. The time complexity is usually expressed as a function of the input's size $n$ (but this does not always have to be the case: for instance, you can express the time complexity as a function ...


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If I understood well you have 2 questions. How to get the bounding box given the network output What Smooth L1 loss is The answer to your first question lies in the equation (2) in the section 3.2.1 from the Faster R-CNN paper. As all anchor based object detector (Faster RCNN, YOLOv3, EfficientNets, FPN...) the regression output from the network are not ...


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The route/trajectory followed by the optimization algorithm basically depends your dataset and the loss function. However, what really matters, for the purpose of final accuracy performance, is the final point which the trajectory converges to.


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Yes, it is not specified because the region proposal algorithm did not change from R-CNN (the previous version from Fast R-CNN, however, in the next verion, Faster R-CNN, this algorithm is replaced by a CNN). The region proposal algorithm you are looking for is called selective search. You can find in the R-CNN paper that the algorithm is described in "...


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From what I understood, you want to be able to determine whether the input to your classifier is a valid picture or not. Where: Valid picture: image of a person wearing or not wearing a seatbelt Not valid picture: unrelated images (say a kitchen picture) or noise, or a black image (no input at all) For that you could build a Bayesian model from your ...


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Yes, a category "no person" or "random image" would make sense. Binary classification is only helpful if you know that your input always belongs to one or the other category, for example by pre-filtering the inputs.


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The problem with certain activation functions, such as the sigmoid, is that they squash the input to a finite interval (i.e. they are sometimes classified as saturating activation functions). For example, the sigmoid function has codomain $[0, 1]$, as you can see from the illustration below. This property/behaviour can lead to the vanishing gradient problem ...


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No, you can't. In CNN, if you want to detect landmark, you need to prepare data with region box, it's coordinates, width, height, than number of points that should be detected and points coordinates. Then your target vector should be, This is your target vector. Optionally you can use YOLO algorithm.


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I have not used fastai library but this also happens on tensorboard when you have more than one training being recorded on the same plot. Looking at the picture, I think this is a very special type of graph because for a single LR value you have 2 loss values associated. Put in other words, you have the same LR value for different loss values. My guess it ...


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One could imagine using a segmentation network as a first step of processing. Then feeding an area corresponding to a bounding box of each segmented object to the classifier. Potentially that could yield an increase in performance in classifying objects in an image, but not without a cost of training time, sine suddenly there are two networks to train ...


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Generally, order matters. A (trained) Neural Network (NN) is just a mathematical function trained on taking some given input and producing the corresponding output. So, if you train a certain node on producing large output if (and only if) an animal is present in a picture (for example), but later you give it the numeric evidence for a car being present in ...


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From how it looks, the most reliable method to try out is using Hough transform. The Hough transform can be used to detect e.g. lines and circles in images (depending on which variant you are using; in this case it would amount to a combination of variants for both lines and circles obviously). So, given some input image, the Hough transform tells you what ...


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Yes! This is crucial. If you rotate your input images for segmentation, you need to rotate the output masks as well. Otherwise the loss of your network will not be correctly calculated and your network will not learn how to generalize to rotated input images. If you use keras, you can use two ImageDataGenerator classes, one for the images and one for the ...


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