Predictions from lower layers help in dealing with smaller sized objects. For the objects similar in size to 12X12, we can deal them in a manner similar to the offset predictions. Learn Machine Learning, AI & Computer vision, Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper, . The one line solution to this is to make predictions on top of every feature map(output after each convolutional layer) of the network as shown in figure 9. Being simple in design, its implementation is more direct from GPU and deep learning framework point of view and so it carries out heavy weight lifting of detection at lightning speed. Remember, conv feature map at one location represents only a section/patch of an image. You can think it as the expected bounding box prediction – the average shape of objects at a certain scale. This will amount to thousands of patches and feeding each of them in a network will result in huge of amount of time required to make predictions on a single image. In the above example, boxes at center (6,6) and (8,6) are default boxes and their default size is 12X12. Each location in this map stores classes confidence and bounding box information as if there is indeed an object of interests at every location. Note that the position and size of default boxes depend upon the network construction. In essence, SSD is a multi-scale sliding window detector that leverages deep CNNs for both these tasks. For SSD512, there are in fact 64x64x4 + 32x32x6 + 16x16x6 + 8x8x6 + 4x4x6 + 2x2x4 + 1x1x4 = 24564 predictions in a single input image. Deep dive into SSD training: 3 tips to boost performance¶. Object detection presents several other challenges in addition to concerns about speed versus accuracy. A simple strategy to train a detection network is to train a classification network. Vanilla squared error loss can be used for this type of regression. Tensorflow object detection API is a powerful tool for creating custom object detection/Segmentation mask model and deploying it, without getting too much into the model-building part. And shallower layers bearing smaller receptive field can represent smaller sized objects. 04. We denote these by. And in order to make these outputs predict cx and cy, we can use a regression loss. Basic knowledge of PyTorch, convolutional neural networks is assumed. The following figure-6 shows an image of size 12X12 which is initially passed through 3 convolutional layers, each with filter size 3×3(with varying stride and max-pooling). Before the renaissance of neural networks, the best detection methods combined robust low-level features (SIFT, HOG etc) and compositional model that is elastic to object deformation. Object detection is a challenging computer vision task that involves predicting both where the objects are in the image and what type of objects were detected. Therefore ground truth for these patches is [0 0 1]. Loss values of ssd_mobilenet can be different from faster_rcnn. We can see there is a lot of overlap between these two patches(depicted by shaded region). By utilising this information, we can use shallow layers to predict small objects and deeper layers to predict big objects, as smal… A sliding window detection, as its name suggests, slides a local window across the image and identifies at each location whether the window contains any object of interests or not. Object Detection using Hog Features: In a groundbreaking paper in the history of computer vision, … My hope is that this tutorial has provided an understanding of how we can use the OpenCV DNN module for object detection. When combined together these methods can be used for super fast, real-time object detection on resource constrained devices (including the Raspberry Pi, smartphones, etc.) In the future, we will look into deploying the trained model in different hardware and … The question is, how? which can thus be used to find true coordinates of an object. The feature extraction network is typically a pretrained CNN (see Pretrained Deep Neural Networks (Deep Learning Toolbox) for … So we can see that with increasing depth, the receptive field also increases. . So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. So let’s look at the method to reduce this time. Notice, experts in the same layer take the same underlying input (the same receptive field). I followed this tutorial for training my shoe model. We compute the intersect over union (IoU) between the priorbox and the ground truth. In essence, SSD does sliding window detection where the receptive field acts as the local search window. Single Shot MultiBox Detector (SSD *) is fast and accurate object detection with a single network. SSD (Single Shot MultiBox Detector) is a popular algorithm in object detection. Therefore we first find the relevant default box in the output of feat-map2 according to the location of the object. For this Demo, we will use the same code, but we’ll do a few tweakings. It is used to detect the object and also classifies the detected object. Deep convolutional neural networks can predict not only an object's class but also its precise location. In fact, only the very last layer is different between these two tasks. When we’re shown an image, our brain instantly recognizes the objects contained in it. Training an object detection model can be resource intensive and time-consuming. Train SSD on Pascal VOC dataset¶. We put one priorbox at each location in the prediction map. researchers and engineers. In image classification, we predict the probabilities of each class, while in object detection, we also predict a bounding box containing the object of that class. Let us index the location at output map of 7,7 grid by (i,j). In this case which one or ones should be picked as the ground truth for each prediction? This has two problems. It is first passed through the convolutional layers similar to above example and produces an output feature map of size 6×6. This tutorial will guide you through the steps to detect objects within a directory of image files using Single-Shot Multi-Box Detection (SSD) as described by . If you're new to PyTorch, first read Deep Learning with PyTorch: A 60 Minute Blitz and Learning PyTorch with Examples. If output probabilities are in the order cat, dog, and background, ground truth becomes [1 0 0]. Given an input image, the algorithm outputs a list of objects, each associated with a class label and location (usually in the form of bounding box coordinates). Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. Last but not least, SSD allows feature sharing between the classification task and the localization task. For more information of receptive field, check thisout. In practice, SSD uses a few different types of priorbox, each with a different scale or aspect ratio, in a single layer. The task of object detection is to identify "what" objects are inside of an image and "where" they are. It is notintended to be a tutorial. To understand this, let’s take a patch for the output at (5,5). The patch 2 which exactly contains an object is labeled with an object class. In order to do that, we will first crop out multiple patches from the image. You can add it as a pull request and I will merge it when I get the chance. This will amount to thousands of patches and feeding each of them in a network will result in huge of amount of time required to make predictions on a single image. If you would like to contribute a translation in another language, please feel free! Here we are calculating the feature map only once for the entire image. You can jump to the code and the instructions from here. computation to accelerate human progress. This is achieved with the help of priorbox, which we will cover in details later. This is where priorbox comes into play. Smaller objects tend to be much more difficult to catch, especially for single-shot detectors. For training classification, we need images with objects properly centered and their corresponding labels. You can download the demo from this repo. On the other hand, it takes a lot of time and training data for a machine to identify these objects. And thus it gives more discriminating capability to the network. For example, SSD512 use 4, 6, 6, 6, 6, 4, 4 types of different priorboxes for its seven prediction layers, whereas the aspect ratio of these priorboxes can be chosen from 1:3, 1:2, 1:1, 2:1 or 3:1. We know the ground truth for object detection comes in as a list of objects, whereas the output of SSD is a prediction map. Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. This way we can now tackle objects of sizes which are significantly different than 12X12 size. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. Intuitively, object detection is a local task: what is in the top left corner of an image is usually unrelated to predict an object in the bottom right corner of the image. This means that when they are fed separately(cropped and resized) into the network, the same set of calculations for the overlapped part is repeated. That is called its receptive field size. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. figure 3: Input image for object detection. You could refer to TensorFlow detection model zoo to gain an idea about relative speed/accuracy performance of the models. Then for the patches(1 and 3) NOT containing any object, we assign the label “background”. Here the multibox is a name of the technique for the bounding box regression. So for every location, we add two more outputs to the network(apart from class probabilities) that stands for the offsets in the center. For preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. And all the other boxes will be tagged bg. In general, if you want to classify an image into a certain category, you use image classification. Three sets of this 3X3 filters are used here to obtain 3 class probabilities(for three classes) arranged in 1X1 feature map at the end of the network. Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. Calculating convolutional feature map is computationally very expensive and calculating it for each patch will take very long time. Now during the training phase, we associate an object to the feature map which has the default size closest to the object’s size. The Practitioner Bundle of Deep Learning for Computer Vision with Python discusses the traditional sliding window + image pyramid method for object detection, including how to use a CNN trained for classification as an object detector. This is something well-known to image classification literature and also what SSD is heavily leveraged on. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. Let's first remind ourselves about the two main tasks in object detection: identify what objects in the image (classification) and where they are (localization). It can easily be calculated using simple calculations. This is a PyTorch Tutorial to Object Detection.. Download and install LabelImg, point it to your \images\traindirectory, and then draw a box around each object in each image. Therefore we first find the relevant default box in the output of feat-map2 according to the location of the object. So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. One type refers to the object whose, (default size of the boxes). So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. SSD- Single Shot MultiBox Detector: In this Single Shot MultiBox Detector, we can do the object detection and classification using single forward pass of the network. Let us see how their assignment is done. Some other object detection networks detect objects by sliding different sized boxes across the image and running the classifier many times on different sections.
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