Convolutional networks are hierarchical in nature. The patches for other outputs only partially contains the cat. Secondly, if the object does not fit into any box, then it will mean there won’t be any box tagged with the object. This is where priorbox comes into play. Repeat the process for all the images in the \images\testdirectory. We can see that the object is slightly shifted from the box. Figure 7: Depicting overlap in feature maps for overlapping image regions. In general, if you want to classify an image into a certain category, you use image classification. So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. We will skip this minor detail for this discussion. It is first passed through the convolutional layers similar to above example and produces an output feature map of size 6×6. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. There is, however, some overlap between these two scenarios. researchers and engineers. And then we assign its ground truth target with the class of object. K is computed on the fly for each batch to keep a 1:3 ratio between foreground samples and background samples. This tutorial shows you it can be as simple as annotation 20 images and run a Jupyter notebook on Google Colab. So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. The other type refers to the, as shown in figure 9. This is a PyTorch Tutorial to Object Detection.. And then since we know the parts on penultimate feature map which are mapped to different paches of image, we direcltly apply prediction weights(classification layer) on top of it. Reducing redundant calculations of Sliding Window Method, Training Methodology for modified network. Let’s say in our example, cx and cy is the offset in center of the patch from the center of the object along x and y-direction respectively(also shown). Now that we have taken care of objects at different locations, let’s see how the changes in the scale of an object can be tackled. Lambda provides GPU workstations, servers, and cloud But in this solution, we need to take care of the offset in center of this box from the object center. Create a SSD Object Detection Network The SSD object detection network can be thought of as having two sub-networks. We will look at two different techniques to deal with two different types of objects. Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper https://arxiv.org/abs/1512.02325. Predictions from lower layers help in dealing with smaller sized objects. In our sample network, predictions on top of first feature map have a receptive size of 5X5 (tagged feat-map1 in figure 9). Convolutional networks are hierarchical in nature. 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. . I followed this tutorial for training my shoe model. For more information of receptive field, check thisout. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. In fact, only the very last layer is different between these two tasks. Object detection has … Now since patch corresponding to output (6,6) has a cat in it, so ground truth becomes [1 0 0]. The following figure shows sample patches cropped from the image. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). Let us assume that true height and width of the object is h and w respectively. This creates extra examples of large objects. Remember, conv feature map at one location represents only a section/patch of an image. We repeat this process with smaller window size in order to be able to capture objects of smaller size. Patch with (7,6) as center is skipped because of intermediate pooling in the network. For reference, output and its corresponding patch are color marked in the figure for the top left and bottom right patch. 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. But in this solution, we need to take care of the offset in center of this box from the object center. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. 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. This can easily be avoided using a technique which was introduced in SPP-Net and made popular by Fast R-CNN. Such a brute force strategy can be unreliable and expensive: successful detection requests the right information being sampled from the image, which usually means a fine-grained resolution to slide the window and testing a large cardinality of local windows at each location. If you're new to PyTorch, first read Deep Learning with PyTorch: A 60 Minute Blitz and Learning PyTorch with Examples. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different loca… This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. Now, let’s move ahead in our Object Detection Tutorial and see how we can detect objects in Live Video Feed. was released at the end of November 2016 and reached new records in terms of performance and precision for object detection tasks, scoring over 74% mAP (mean Average Precision) at 59 frames per second on standard datasets such as PascalVOC and COCO. We will skip this minor detail for this discussion. So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. But, using this scheme, we can avoid re-calculations of common parts between different patches. We put one priorbox at each location in the prediction map. The class of the ground truth is directly used to compute the classification loss; whereas the offset between the ground truth bounding box and the priorbox is used to compute the location loss. In practice, SSD uses a few different types of priorbox, each with a different scale or aspect ratio, in a single layer. We denote these by. We name this because we are going to be referring it repeatedly from here on. This basically means we can tackle an object of a very different size by using features from the layer whose receptive field size is similar. So, we have 3 possible outcomes of classification [1 0 0] for cat, [0 1 0] for dog and [0 0 1] for background. This concludes an overview of SSD from a theoretical standpoint. Earlier we used only the penultimate feature map and applied a 3X3 kernel convolution to get the outputs(probabilities, center, height, and width of boxes). As you can imagine this is very resource-consuming. Each location in this map stores classes confidence and bounding box information as if there is indeed an object of interests at every location. Have covered this algorithm can help in dealing with smaller sized objects a moment we... 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