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. This will help us solve the problem of size and location. So the boxes which are directly represented at the classification outputs are called default boxes or anchor boxes. SSD In order to preserve real-time speed without sacrificing too much detection accuracy, Liu et al. 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). We were able to run this in real time on videos for pedestrian detection, face detection, and so many other object detection use-cases. Hence, we know both the class and location of the objects in the image. So, RPN gives out bounding boxes of various sizes with the corresponding probabilities of each class. In some recent posts of your blog you used caffe model in opencv. This can easily be avoided using a technique which was introduced in SPP-Net and made popular by Fast R-CNN. Well, there are a few more problems. So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. SSD(Single Shot Detector) YOLOより高速である。 Faster RCNNと同等の精度を実現。 セマンティックセグメンテーション. In this post, I shall explain object detection and various algorithms like Faster R-CNN, YOLO, SSD. However, we still won’t know the location of cat or dog. And shallower layers bearing smaller receptive field can represent smaller sized objects. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. Why do we have so many methods and what are the salient features of each of these? And in order to make these outputs predict cx and cy, we can use a regression loss. which can thus be used to find true coordinates of an object. Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper https://arxiv.org/abs/1512.02325. Hence, the network only fine-tuned the fully connected part of the network. But, using this scheme, we can avoid re-calculations of common parts between different patches. Since we had modeled object detection into a classification problem, success depends on the accuracy of classification. Also, the key points of this algorithm can help in getting a better understanding of other state-of-the-art methods. First of all a visual understanding of speed vs accuracy trade-off: SSD seems to be a good choice as we are able to run it on a video and the accuracy trade-off is very little. Vanilla squared error loss can be used for this type of regression. Now, we run a small 3×3 sized convolutional kernel on this feature map to foresee the bounding boxes and categorization probability. Model attributes are coded in their names. At each location, the original paper uses 3 kinds of anchor boxes for scale 128x 128, 256×256 and 512×512. So, In total at each location, we have 9 boxes on which RPN predicts the probability of it being background or foreground. Hint. So we can see that with increasing depth, the receptive field also increases. For the sake of argument, let us assume that we only want to deal with objects which are far smaller than the default size. 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. SSD (Single Shot Multibox Detector) Overview. In this tutorial, we will also use the Multi-Task Cascaded Convolutional Neural Network, or MTCNN, for face detection, e.g. Similarly, predictions on top of feature map feat-map2 take a patch of 9X9 into account. There is a minor problem though. And all the other boxes will be tagged bg. Well, it’s faster. For reference, output and its corresponding patch are color marked in the figure for the top left and bottom right patch. In a previous post, we covered various methods of object detection using deep learning. they made it possible to train end-to-end. Let’s have a look: In a groundbreaking paper in the history of computer vision, Navneet Dalal and Bill Triggs introduced Histogram of Oriented Gradients(HOG) features in 2005. However, there was one problem. To summarize we feed the whole image into the network at one go and obtain feature at the penultimate map. After the rise of deep learning, the obvious idea was to replace HOG based classifiers with a more accurate convolutional neural network based classifier. Not all patches from the image are represented in the output. For preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. The patches for other outputs only partially contains the cat. After the classification network is trained, it can then be used to carry out detection on a new image in a sliding window manner. We then feed these patches into the network to obtain labels of the object. Now, all these windows are fed to a classifier to detect the object of interest. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. However, there was one problem. YOLO also predicts the classification score for each box for every class in training. So we resort to the second solution of tagging this patch as a cat. So just like before, we associate default boxes with different default sizes and locations for different feature maps in the network. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. We name this because we are going to be referring it repeatedly from here on. Especially, the train, eval, ssd, faster_rcnn and preprocessing protos are important when fine-tuning a model. Which one should you use? So the images(as shown in Figure 2), where multiple objects with different scales/sizes are present at different locations, detection becomes more relevant. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). It has been explained graphically in the figure. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. For the objects similar in size to 12X12, we can deal them in a manner similar to the offset predictions. Now let’s consider multiple crops shown in figure 5 by different colored boxes which are at nearby locations. To solve this problem an image pyramid is created by scaling the image. We repeat this process with smaller window size in order to be able to capture objects of smaller size. . And thus it gives more discriminating capability to the network. Here we are applying 3X3 convolution on all the feature maps of the network to get predictions on all of them. So let’s take an example (figure 3) and see how training data for the classification network is prepared. The second patch of 12X12 size from the image located in top right quadrant(shown in red, center at 8,6) will correspondingly produce 1X1 score in final layer(marked in red). Now, we can feed these boxes to our CNN based classifier. 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. Let’s have a look at them: For YOLO, detection is a simple regression problem which takes an input image and learns the class probabilities and bounding box coordinates. We denote these by. It was impossible to run CNNs on so many patches generated by sliding window detector. Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Learn Machine Learning, AI & Computer vision, What would our model predict? Therefore ground truth for these patches is [0 0 1]. paper: summary: Adversarial Semantic Data Augmentation for Human Pose Estimation. Face detection is the process of automatically locating faces in a photograph and localizing them by drawing a bounding box around their extent.. So let’s look at the method to reduce this time. Here we are taking an example of a bigger input image, an image of 24X24 containing the cat(figure 8). Historically, there have been many approaches to object detection starting from Haar Cascades proposed by Viola and Jones in 2001. 1000-mixup_pytorch: A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch. 1 YOLACT++ Better Real-time Instance Segmentation Daniel Bolya , Chong Zhou , Fanyi Xiao, and Yong Jae Lee Abstract—We present a simple, fully-convolutional model for real-time (> 30 fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach. Was very slow figure shows sample patches cropped from the box object in an image of 24X24 the. Understanding of other state-of-the-art methods the true height and width like performing sliding window Detector were! Above example and produces an output feature map to predict the bounding and. 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