5400+ faster-rcnn.pytorch: This project is a faster faster R-CNN implementation, aimed to accelerating the training of faster R-CNN object detection models. The remaining network is similar to Fast-RCNN. R-CNN solves this problem by using an object proposal algorithm called. 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. Predictions from lower layers help in dealing with smaller sized objects. 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. For preparing training set, first of all, we need to assign the ground truth for all the predictions in classification output. Now, we can feed these boxes to our CNN based classifier. The papers on detection normally use smooth form of L1 loss. In a previous post, we covered various methods of object detection using deep learning. . Tagging this as background(bg) will necessarily mean only one box which exactly encompasses the object will be tagged as an object. Hence, there are 3 important parts of R-CNN: Fast RCNN uses the ideas from SPP-net and RCNN and fixes the key problem in SPP-net i.e. Now let’s consider multiple crops shown in figure 5 by different colored boxes which are at nearby locations. SSD also uses anchor boxes at various aspect ratio similar to Faster-RCNN and learns the off-set rather than learning the box. Now, we can feed these boxes to our CNN based classifier. How do you know the size of the window so that it always contains the image? Hint. It is like performing sliding window on convolutional feature map instead of performing it on the input image. 1000-mixup_pytorch: A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch. SPP-Net paved the way for more popular Fast RCNN which we will see next. So, for each instance of the object in the image, we shall predict following variables: Just like multi-label image classification problems, we can have multi-class object detection problem where we detect multiple kinds of objects in a single image: In the following section, I will cover all the popular methodologies to train object detectors. We repeat this process with smaller window size in order to be able to capture objects of smaller size. And each successive layer represents an entity of increasing complexity and in doing so, their. Notice that at runtime, we have run our image on CNN only once. which can thus be used to find true coordinates of an object. SSD runs a convolutional network on input image only one time and computes a feature map. introduced Histogram of Oriented Gradients(HOG) features in 2005. Slowest part in Fast RCNN was, . Sounds simple! In order to handle the scale, SSD predicts bounding boxes after multiple convolutional layers. 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. . But in this solution, we need to take care of the offset in center of this box from the object center. We already know the default boxes corresponding to each of these outputs. 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. So predictions on top of penultimate layer in our network have maximum receptive field size(12X12) and therefore it can take care of larger sized objects. This method, although being more intuitive than its counterparts like faster-rcnn, fast-rcnn(etc), is a very powerful algorithm. However, we shall be focussing on state-of-the-art methods all of which use neural networks and Deep Learning. Object Detection is modeled as a classification problem where we take windows of fixed sizes from input image at all the possible locations feed these patches to an image classifier. We then feed these patches into the network to obtain labels of the object. It uses spatial pooling after the last convolutional layer as opposed to traditionally used max-pooling. There is one more problem, aspect ratio. Why do we have so many methods and what are the salient features of each of these? So, the output of the network should be: Class probabilities should also include one additional label representing background because a lot of locations in the image do not correspond to any object. First, a feature pyramid architecture based on Single Shot MultiBox Detector (SSD) is used to improve the detection performance, and … CNNs were too slow and computationally very expensive. So, In total at each location, we have 9 boxes on which RPN predicts the probability of it being background or foreground. 论文地址:DSSD: Deconvolutional Single Shot Detector概述这篇论文应该算是SSD: Single Shot MultiBox Detector的第一个改进分支,作者是Cheng-Yang Fu, 我们熟知的Wei Liu大神在这里面是第二作者,说明是一个团队的成果,论文很新,暂未发布源代码。博主对该文章重要部分做了翻译理解工作,不一定完全对,欢迎讨论。 So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. One type refers to the object whose, (default size of the boxes). Single Shot Multibox Detector (SSD) with MobileNet, SSD with Inception V2, Region-Based Fully Convolutional Networks (R-FCN) with Resnet 101, Faster RCNN with Resnet 101, Faster RCNN with Inception Resnet v2; Frozen weights (trained on the COCO dataset) for each of the above models to be used for out-of-the-box inference purposes. Vanilla squared error loss can be used for this type of regression. SSD or Single Shot Detector is a multi-box approach used for real-life object detection. Work proposed by Christian Szegedy is presented in a more comprehensible manner in the SSD paper https://arxiv.org/abs/1512.02325. Hog features are computationally inexpensive and are good for many real-world problems. So for every location, we add two more outputs to the network(apart from class probabilities) that stands for the offsets in the center. To solve this problem we can train a multi-label classifier which will predict both the classes(dog as well as cat). Therefore we first find the relevant default box in the output of feat-map2 according to the location of the object. So this saves a lot of computation. Since each convolutional layer operates at a different scale, it is able to detect objects of various scales. In a previous post, we covered various methods of object detection using deep learning. However, most of these boxes have low confidence scores and if we set a threshold say 30% confidence, we can remove most of them as shown in the example below. YOLO also predicts the classification score for each box for every class in training. We need to devise a way such that for this patch, the network can also predict these offsets which can thus be used to find true coordinates of an object. Remember, fully connected part of CNN takes a fixed sized input so, we resize(without preserving aspect ratio) all the generated boxes to a fixed size (224×224 for VGG) and feed to the CNN part. The other type refers to the objects whose size is significantly different from 12X12. Single Shot Detectors: 211% faster object detection with OpenCV’s ‘dnn’ module and an NVIDIA GPU. The other type refers to the, as shown in figure 9. Let us assume that true height and width of the object is h and w respectively. Since the number of bins remains the same, a constant size vector is produced as demonstrated in the figure below. 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. So we can see that with increasing depth, the receptive field also increases. Now, we shall take a slightly bigger image to show a direct mapping between the input image and feature map. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. The following figure shows sample patches cropped from the image. However, if the object class is not known, we have to not only determine the location but also predict the class of each object. So it is about finding all the objects present in an image, predicting their labels/classes and assigning a bounding box around those objects. Now, this is how we need to label our dataset that can be used to train a convnet for classification. We apply bounding box regression to improve the anchor boxes at each location. 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. Hog features are computationally inexpensive and are good for many real-world problems. CNNs were too slow and computationally very expensive. However, there are a few methods that pose detection as a regression problem. We were able to run this in real time on videos for pedestrian detection, face detection, and so many other object detection use-cases. Fast RCNN uses the ideas from SPP-net and RCNN and fixes the key problem in SPP-net i.e. Here we are calculating the feature map only once for the entire image. In this post, I shall explain object detection and various algorithms like Faster R-CNN, YOLO, SSD. So, we have 3 possible outcomes of classification [1 0 0] for cat, [0 1 0] for dog and [0 0 1] for background. However, one limitation for YOLO is that it only predicts 1 type of class in one grid hence, it struggles with very small objects. 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. So we assign the class “cat” to patch 2 as its ground truth. Hence, there are 3 important parts of R-CNN: Still, RCNN was very slow. 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 can easily be avoided using a technique which was introduced in. If output probabilities are in the order cat, dog, and background, ground truth becomes [1 0 0]. In this blog, I will cover Single Shot Multibox Detector in more details. And How does it achieve that? We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. This concludes an overview of SSD from a theoretical standpoint. As you can see, different 12X12 patches will have their different 3X3 representations in the penultimate map and finally, they produce their corresponding class scores at the output layer. SSD is one of the most popular object detection algorithms due to its ease of implementation and good accuracy vs computation required ratio. I recently noticed that Opencv in version 3.4.2 support one of the best and most accurate tensorflow models: Faster rcnn inception v2 in object detection. Now, all these windows are fed to a classifier to detect the object of interest. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. One more thing that Fast RCNN did that they added the bounding box regression to the neural network training itself. However, there was one problem. SSD also uses anchor boxes at various aspect ratio similar to Faster-RCNN and learns the off-set rather than learning the box. Here we are calculating the feature map only once for the entire image. Object Detection is the backbone of many practical applications of computer vision such as autonomous cars, security and surveillance, and many industrial applications. Could on please make a post on implementation of faster rcnn inception v2 on Opencv? Choice of a right object detection method is crucial and depends on the problem you are trying to solve and the set-up. Let’s increase the image to 14X14(figure 7). Especially, the train, eval, ssd, faster_rcnn and preprocessing protos are important when fine-tuning a model. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). . The varying sizes of bounding boxes can be passed further by apply Spatial Pooling just like Fast-RCNN. Then we crop the patches contained in the boxes and resize them to the input size of classification convnet. The box does not exactly encompass the cat, but there is a decent amount of overlap. That is called its receptive field size. SSD In order to preserve real-time speed without sacrificing too much detection accuracy, Liu et al. Dealing with objects very different from 12X12 size is a little trickier. There was one more challenge: we need to generate the fixed size of input for the fully connected layers of the CNN so, SPP introduces one more trick. This concludes an overview of SSD from a theoretical standpoint. And in order to make these outputs predict cx and cy, we can use a regression loss. Therefore ground truth for these patches is [0 0 1]. We can see there is a lot of overlap between these two patches(depicted by shaded region). 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. However, we still won’t know the location of cat or dog. So for its assignment, we have two options: Either tag this patch as one belonging to the background or tag this as a cat. feature map just before applying classification layer. On top of this 3X3 map, we have applied a convolutional layer with a kernel of size 3X3. You can combine both the classes to calculate the probability of each class being present in a predicted box. So, now the network had two heads, classification head, and bounding box regression head. 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. We repeat this process with smaller window size in order to be able to capture objects of smaller size. The patch 2 which exactly contains an object is labeled with an object class. To handle the variations in aspect ratio and scale of objects, Faster R-CNN introduces the idea of anchor boxes. Let us see how their assignment is done. So for example, if the object is of size 6X6 pixels, we dedicate feat-map2 to make the predictions for such an object. A lot of objects can be present in various shapes like a sitting person will have a different aspect ratio than standing person or sleeping person. Well, it’s faster. These two changes reduce the overall training time and increase the accuracy in comparison to SPP net because of the end to end learning of CNN. While classification is about predicting label of the object present in an image, detection goes further than that and finds locations of those objects too. Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Remember, conv feature map at one location represents only a section/patch of an image. 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. And all the other boxes will be tagged bg. Now during the training phase, we associate an object to the feature map which has the default size closest to the object’s size. Model attributes are coded in their names. So we resort to the second solution of tagging this patch as a cat. For training classification, we need images with objects properly centered and their corresponding labels. Similarly, for aspect ratio, it uses three aspect ratios 1:1, 2:1 and 1:2. 论文题目:SSD: Single Shot MultiBox Detector 论文链接:论文链接 ...This results in a significant improvement in speed for high-accuracy detection(59 FPS with mAP 74.3% on VOC2007 test, vs Faster... 【深度】YOlOv4导读与论文翻译 And in order to make these outputs predict cx and cy, we can use a regression loss. It is first passed through the convolutional layers similar to above example and produces an output feature map of size 6×6. For example, when we built a cat-dog classifier, we took images of cat or dog and predicted their class: What do you do if both cat and dog are present in the image: What would our model predict? The SSD architecture was published in 2016 by researchers from Google. 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). This technique ensures that any feature map do not have to deal with objects whose size is significantly different than what it can handle. Currently, Faster-RCNN is the choice if you are fanatic about the accuracy numbers. 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). Reducing redundant calculations of Sliding Window Method, Training Methodology for modified network. 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. R-CNN solves this problem by using an object proposal algorithm called Selective Search which reduces the number of bounding boxes that are fed to the classifier to close to 2000 region proposals. Let us understand this in detail. Here is a gif that shows the sliding window being run on an image: We will not only have to take patches at multiple locations but also at multiple scales because the object can be of any size. Firstly the training will be highly skewed(large imbalance between object and bg classes). We have seen this in our example network where predictions on top of penultimate map were being influenced by 12X12 patches. Here is a quick comparison between various versions of RCNN. Since we had modeled object detection into a classification problem, success depends on the accuracy of classification. Also, SSD paper carves out a network from VGG network and make changes to reduce receptive sizes of layer(atrous algorithm). Hopefully, this post gave you an intuition and understanding behind each of the popular algorithms for object detection. To see our Single Shot Detector in action, make sure you use the “Downloads” section of this tutorial to download (1) the source code and (2) pretrained models compatible with OpenCV’s dnn module. We can see there is a lot of overlap between these two patches(depicted by shaded region). That is called its. There is a minor problem though. … It has been explained graphically in the figure. After the rise of deep learning, the obvious idea was to replace HOG based classifiers with a more accurate convolutional neural network based classifier. What size do you choose for your sliding window detector? Object Detection is the backbone of many practical applications of computer vision such as autonomous cars, security and surveillance, and many industrial applications. 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