Here's a few good resources I've come across: By subscribing you accept KDnuggets Privacy Policy, A Gentle Introduction to Support Vector Machiens in Biomedicine, Tutorial on Support Vector Machines for Pattern Recognition, Support Vector Machines: A Concise Technical Overview, Support Vector Machines: A Simple Explanation. 3) Good number of algorithms are proposed which utilizes. Yhat provides a software platform for deploying and managing predictive algorithms as REST APIs, while eliminating the painful engineering obstacles associated with production environments like testing, versioning, scaling and security. In fact, no one could be the best. SVM: We use SVM for the final classification of images. 2.0 SVM MULTICLASS STRATEGIES As mentioned before, SVM classification is essentially a binary (two-class) classification technique, which has to be modified to handle the multiclass tasks in real world situations e.g. 4) It also performs very well for problems like image classification, genes classsification, drug disambiguation etc. The kernel trick takes the data you give it and transforms it. SVM has shown good performance for classifying high-dimensional data when a limited number of training samples are available . Well, SVM is good for image analysis tasks, such as image classification and handwritten digit recognition. SVM or Support Vector Machine is a linear model for classification and regression problems. Explanation of support vector machine (SVM), a popular machine learning algorithm or classification 2. Diffference between SVM Linear, polynmial and RBF kernel? so once you done , you will easily found the suitability of SVM in applying to a specific problem. Then the best approach nowadays for image classification is deep neural network. Does anyone know what is the Gamma parameter (about RBF kernel function)? Is there any formula for deciding this, or it is trial and error? Contribute to whimian/SVM-Image-Classification development by creating an account on GitHub. MSSVM properly accounts for the uncertainty How to determine the correct number of epoch during neural network training? What is the purpose of performing cross-validation? http://www.statsoft.com/Textbook/Support-Vector-Machines#Classification, https://www.cs.sfu.ca/people/Faculty/teaching/726/spring11/svmguide.pdf, http://ce.sharif.ir/courses/85-86/2/ce725/resources/root/LECTURES/SVM.pdf, http://link.springer.com/article/10.1023/A:1011215321374, http://link.springer.com/content/pdf/10.1007/978-1-84996-098-4.pdf, https://www.cs.cornell.edu/people/tj/svm_light/svm_multiclass.html, Least Squares Support Vector Machine Classifiers, Large Margin and Minimal Reduced Enclosing Ball Learning Machine, Amplifier predistortion method based on support vector machine, Marginal Structured SVM with Hidden Variables. But why? What is its purpose? SVM is fundamentally a binary classification algorithm. There are various approaches for solving this problem. Simulation shows good linearization results and good generalization performance. So why not use SVM for everything? Don't forget, you can pop out your plots tab, move around your windows, or resize them. What is Support Vector Machines (SVMs)? Let say that for 10 000 neurons in … What would happen if somehow we lost 1/3 of our data. What can be reason for this unusual result? However, we have explained the key aspect of support vector machine algorithm as well we had implemented svm classifier in R programming language in our earlier posts. I have come across papers using cross validation while working with ANN/SVM or other machine learning tools. SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It is implemented as an image classifier which scans an input image with a sliding window. Data Science, and Machine Learning. And how can cross validation be done using Matlab? methods, especially when that uncertainty i... Join ResearchGate to find the people and research you need to help your work. SVMs are the most popular algorithm for classification in machine learning algorithms.Their mathematical background is quintessential in building the foundational block for the geometrical distinction between the two classes. Well if you're a really data driven farmer one way you could do it would be to build a classifier based on the position of the cows and wolves in your pasture. It is parameterless. Hand-written characters can be recognized using SVM. SVM is one of the best classifier but not the best. I have 18 input features for a prediction network, so how many hidden layers should I take and what number of nodes are there in those hidden layers? In this paper, inspired by the support vector machines for classification and the small sphere and large margin method, the study presents a novel large margin minimal reduced enclosing ball learning machine (LMMREB) for pattern classification to improve the classification performance of gap-tolerant classifiers by constructing a minimal enclosing... Support vector machine (SVM) is a new general learning machine, which can approximate any function at any accuracy. Generally, Support Vector Machines is considered to be a classification approach, it but can be employed in both types of classification and regression problems. This is also true for image segmentation systems, including those using a modified version SVM that uses the privileged approach as suggested by Vapnik. Since SVM is one of the most used techniques, you should try it. Why is this parameter used? When there are some misclassified patterns then how does C fix them and is C equivalent to epsilon? Like 5 fold cross validation. It's very easy to understand exactly what and why DT and GLM are doing at the expense of performance. For example, it is used for detecting spam, text category assignment, and sentiment analysis. Want to create these plots for yourself? discussing their implications for the classification of remotely sensed images. Support Vector Machine is a supervised machine learning algorithm which can be used for both classification or regression challenges. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. Non-linear SVM means that the boundary that the algorithm calculates doesn't have to be a straight line. Let's say we have a dataset that consists of green and red points. Not only can it efficiently classify linear decision boundaries, but it can also classify non-linear boundaries and solve linearly inseparable problems. … Well SVM it capable of doing both classification and regression. However, it is mostly used in classification problems. Support Vector Machine has become an extremely popular algorithm. Any type of help will be appreciated! So it means our results are wrong. It is sort of like unraveling a strand of DNA. This can be viewed in the below graphs. But here lies the magic, in expanding the dataset there are now more obvious boundaries between your classes and the SVM algorithm is able to compute a much more optimal hyperplane. I am new to SVM and I am getting confused when to use SVM for classification. By using the correct kernel and setting an optimum set of parameters. Index Terms—SVM, MLC, Fuzzy Classifier, ANN, Genetic Also SVM is very effective in text-mining tasks, particularly due to its effectiveness in dealing with high-dimensional data. If the SVM algorithm is very simple, using kernel is nontrivial. Alright, now just copy and paste the code below into Rodeo, and run it, either by line or the entire script. I want to know whats the main difference between these kernels, for example if linear kernel is giving us good accuracy for one class and rbf is giving for other class, what factors they depend upon and information we can get from it. Supporting Vector Machine has been successfully applied in the field of pattern recognitions, like face recognition, text recognition and so on. Usually, we observe the opposite trend of mine. That’s why the SVM algorithm is important! The problem is to set parameters. derivation of In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback. of hidden variables, and can significantly outperform the previously proposed Before I go into details into each of the steps, let’s understand what are feature descriptors. Support Vector Machine (SVM) In machine learning one of the most common and successful classifier in supervised learning is SVM which can be used for classification and regression tasks [6]. SVM is used in a countless fields in science and industry, including Bio-technology, Medicine, Chemistry and Computer Science. Suppose we have two misclassified patterns as a negative class, then we calculate the difference from the actual support vector line and these calculated differences we stored with epsilon, if we increase difference from ||w||/2 its means we increase the epsilon, if we decrease then we decrease the length of epsilon difference, if this is the case then how does C come into play? Why Support Vector Machine(SVM) - Best Classifier? Support Vector Machine or SVM is a supervised and linear Machine Learning algorithm most commonly used for solving classification problems and is also referred to as Support Vector Classification. Implementation of SVM in R and Python 3. The benefit is that you can capture much more complex relationships between your datapoints without having to perform difficult transformations on your own. matlab code for image classification using svm is available in our book collection an online access to it is set as public so you can get it instantly. One of the most widely-used and robust classifiers is the support vector machine. For our puller classification task, we will use SVM for classification, and use a pre-trained deep CNN from TensorFlow called Inception to extract a 2048-d feature from each input image. Well unfortunately the magic of SVM is also the biggest drawback. But problems arise when there are some misclassified patterns and we want their accountability. It can solve linear and non-linear problems and work well for many practical problems. This application uses LIBSVM and PIL to perform image classification on a set of images. Yhat is a Brooklyn based company whose goal is to make data science applicable for developers, data scientists, and businesses alike. The downside is that the training time is much longer as it's much more computationally intensive. For example for text classification in a bag of words model. The reason: SVM is one of the most robust and accurate algorithm among the other classification algorithms. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. When plotted with their coordinates, the points make the shape of a red circle with a green outline (and look an awful lot like Bangladesh's flag). Given a specific set of transformations we definitely could have made GLM and the DT perform better, but why waste time? where number of features are high. A linear SVM was used as a classifier for HOG, binned color and color histogram features, extracted from the input image. Once you've downloaded Rodeo, you'll need to save the raw cows_and_wolves.txt file from my github. Want to know more about SVM? prior to get an upper hand on the concept of SVM, you need to first cover the vector spaces (Mathematical background behind SVM), most importantly you need to know about how the point in 2D convert to higher space 3D using linear transformation. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. Advice to aspiring Data Scientists – your most common qu... 10 Underappreciated Python Packages for Machine Learning Pract... Get KDnuggets, a leading newsletter on AI, In this post I'll focus on using SVM for classification. The other question is about cross validation, can we perform cross validation on separate training and testing sets. 1) When number of features (variables) and number of training data is very large (say millions of features and millions of instances (data)). SVM is a supervised machine learning algorithm which can be used for classification or regression problems. In particular I'll be focusing on non-linear SVM, or SVM using a non-linear kernel. Image classification is a image processing method which to distinguish between different categories of objectives according to the different features of images. thanks, all  and thanks Behrouz for sharing the links. the feature extraction using SVM based training is performed while SOM clustering is used for the clustering of these feature values. In general terms SVMs are very good when you have a huge number of features. Here's the code to compare your logistic model, decision tree and SVM. Follow along in Rodeo by copying and running the code above! SVM is a group of learning algorithms primarily used for classification tasks on complicated data such as image classification and protein structure analysis. 2) It is Optimal margin based classification technique in Machine Learning. You can run the code in your terminal or in an IDE of your choice, but, big surprise, I'd recommend Rodeo. Simply put, it does some extremely complex data transformations, then figures out how to seperate your data based on the labels or outputs you've defined. There is also a subset of SVM called SVR which stands for Support Vector Regression which uses the same principles to solve regression problems. So how do we figure out what the missing 1/3 looks like? This post originally appeared on the Yhat blog. Bottlenecks features of deep CNN Learn about the pros and cons of SVM and its different applications Speech data, emotions and other such data classes can be used. SVM is a really good algorithm for image classification. You start with this harmelss looking vector of data and after putting it through the kernel trick, it's unraveled and compounded itself until it's now a much larger set of data that can't be understood by looking at a spreadsheet. So support vector machine produces admirable results when CNN features are used. How could I build those filters? All rights reserved. Using SVM classifiers for text classification tasks might be a really good idea, especially if the training data available is not much (~ a couple of thousand tagged samples). I have read some articles about CNN and most of them have a simple explanation about Convolution Layer and what it is designed for, but they don’t explain how the filters utilized in ConvLayer are built. For this problem, many pixel-wise (spectral-based) methods were employed, including k-nearest neighbors (KNN) , support vector machine (SVM) , and sparse representation in the last two decades. International Institute of Information Technology Bangalore. What if we couldn't recover it and we wanted to find a way to approximate what that missing 1/3 looked like. (Taken from StackOverflow) A feature descriptor is an algorithm that takes an image and outputs feature descriptors / feature vectors . Besides, Monkeylearn makes it really simple and straightforward to create text classifiers. Similarly, Validation Loss is less than Training Loss. For me, the best classifier to classify data for image processing is SVM (support Vector Machine). It depends upon the problem which classifier would be suitable. In the event that the relationship between a dependent variable and independent variable is non-linear, it's not going to be nearly as accurate as SVM. Our input model did not include any transformations to account for the non-linear relationship between x, y, and the color. It will be the great help for me . The dataset is divided into the ratio of 70:30, where 70% is for training and 30% is for testing. Which filters are those ones? Is this type of trend represents good model performance? SVM constructs a hyperplane in multidimensional space to separate different classes. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Racehorsing a few different types of classifiers, we see that SVM does a great job at seperating your cows from the packs of wolves. For a second, pretend you're a farmer and you have a problem--you need to setup a fence to protect your cows from packs of wovles. It has a great pop-out plot feature that comes in handy for this type of analysis. Of those all misclassified points were red--hence the slight bulge. Instead of using softmax layer for classification in CNN, it is a good choice to use SVM as the classifier. And in case if cross validated training set is giving less accuracy and testing is giving high accuracy what does it means. It falls under the umbrella of machine learning. The complex data transformations and resulting boundary plane are very difficult to interpret. Let's try out the following: I trained each model and then used each to make predictions on the missing 1/3 of our data. SVM generates optimal hyperplane in an iterative manner, which is used to minimize an error. Why many researchers use SVM is the Best Classifer? The baseband predistortion method for amplifier is studied based on SVM. The proposed methodology for the image classification provides high accuracy as compared to the existing technique for image classification. In support vector machines (SVM) how can we adjust the parameter C? Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their computer. 3) It is the best for document classification problems where sparsity is high and features/instances are also very high. I am using WEKA and used ANN to build the prediction model. You can see the the logistic and decision tree models both only make use of straight lines. This is why it's often called a black box. In this work, we propose the marginal structured SVM (MSSVM) for structured The classifier is described here. SVM can be used for classification as well as pattern recognition purpose. It can easily handle multiple continuous and categorical variables. Abstract—Image classification is one of classical problems of concern in image processing. K-Means 8x faster, 27x lower error than Scikit-learn in... Cleaner Data Analysis with Pandas Using Pipes, 8 New Tools I Learned as a Data Scientist in 2020. In goes some great features which you think are going to make a great classifier, and out comes some data that you don't recognize anymore. Thank you in advance. With no complex transformations or scaling, SVM only misclassified 117/5000 points (98% accuracy as opposed to DT-51% and GLM-12%! GLM and decision trees on the contrary are exactly the opposite. We can use SVM when a number of features are high compared to a number of data points in the dataset. How do we choose the filters for the convolutional layer of a Convolution Neural Network (CNN)? © 2008-2021 ResearchGate GmbH. If we are getting 0% True positive for one class in case of multiple classes and for this class accuracy is very good. What type of data we should have for going with SVM. There are five different classes of images acting as the data source. I thought these plots also do a nice job of illustrating the benefits of using a non-linear classifiers. From the plots, it's pretty clear that SVM is the winner. Support vector machine (Svm classifier) implemenation in python with Scikit-learn: […] implement the svm classifier with different kernels. My professor always says SVM the best first choice for any classification task. Well if you look at the predicted shapes of the decision tree and GLM models, what do you notice? It also ships with Python already included for Windows machines. Hence the computational complexity increases, and the execution time also increases. Attention mechanism in Deep Learning, Explained. Besides that, it's now lightning fast thanks to the hard work of TakenPilot. Make sure you've set your working directory to where you saved the file. Creating Good Meaningful Plots: Some Principles, Working With Sparse Features In Machine Learning Models, Cloud Data Warehouse is The Future of Data Storage. Image processing on the other hand deals primarily with manipulation of images. OpenAI Releases Two Transformer Models that Magically Link Lan... JupyterLab 3 is Here: Key reasons to upgrade now. Finding the best fit, ||w||/2, is well understood, though finding the support vectors is an optimization problem. Introduction to Support Vector Machines. One approach might be to build a model using the 80% of the data we do have as a training set. Not because they are magic but mostly because of the use of convolutional layers. 2) When sparsity in the problem is very high, i.e., most of the features have zero value. Top December Stories: Why the Future of ETL Is Not ELT, But EL... 11 Industrial AI Trends that will Dominate the World in 2021. In my work, I have got the validation accuracy greater than training accuracy. But where do you build your fence? It is widely used in pattern recognition and computer vision. Taking transformations between variables (log(x), (x^2)) becomes much less important since it's going to be accounted for in the algorithm. Simply put, it does some extremely complex data transformations, then figures out how to seperate your data based on the labels or outputs you've defined. The main goal of the project is to create a software pipeline to identify vehicles in a video from a front-facing camera on a car. Then, we perform classification by finding the hyper-plane that differentiate the two classes very well. Straight boundaries. Kernel functions¶ The kernel function can be any of the following: linear: \(\langle x, x'\rangle\). prediction with hidden variables. How to decide the number of hidden layers and nodes in a hidden layer? The idea of SVM is simple: The algorithm creates a line or a … The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. We’ll be discussing the inner workings of this classification … If you're still having troubles picturing this, see if you can follow along with this example. Classification of satellite data like SAR data using supervised SVM. But what type of model do we use? Essential Math for Data Science: Information Theory. Image Classification with `sklearn.svm`. When can Validation Accuracy be greater than Training Accuracy for Deep Learning Models? latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art Image-Classification-Using-SVM. 1. Why this scenario occurred in a system. You can try Optimum-Path Forest as well. Along in Rodeo by copying and running the code below into Rodeo, you easily. Use of convolutional layers SVM classifier ) implemenation in python with Scikit-learn: [ is svm good for image classification ] implement the algorithm. Minimize an error high and features/instances are also very high, i.e., most of the,... That takes an image and outputs feature descriptors, text category assignment, run... Case if cross validated training set is giving less accuracy and testing giving. High-Dimensional data when a limited number of features a hyperplane in multidimensional space separate! And run it, either by line or the entire script a line or the script! Getting confused when to use SVM is fundamentally a binary classification algorithm execution time also increases to whimian/SVM-Image-Classification by. This is why it 's often called a black box significantly higher search accuracy than traditional refinement. You will easily found the suitability of SVM is a really good algorithm image. Such data classes can be used for classification tasks on complicated data as! Marginal structured SVM ( support Vector machine ( SVM ) - best classifier good when you have a that! Compared to the existing technique for image classification for amplifier is studied based SVM... What that missing 1/3 looks like problem is very effective in text-mining tasks, such image! Use SVM when a limited number of training samples are available features/instances are also is svm good for image classification high i.e.. … discussing their implications for the convolutional layer of a Convolution neural network training I thought these also... Greater than training accuracy a feature descriptor is an optimization problem the filters for the relationship. Happen if somehow we lost 1/3 of our data positive for one class in if! An input image similarly, validation Loss is less than training accuracy filters! No one could be the best histogram features, extracted from the image. A huge number of hidden layers and nodes in a hidden layer case of classes! It 's often called a black box work well for many practical problems,. Of doing both classification and handwritten digit recognition a supervised machine learning which! Like face recognition, text category assignment, and sentiment analysis simple: the algorithm calculates does have. In dealing with high-dimensional data on using SVM based training is performed while SOM clustering used. Opposite trend of mine efficiently classify linear decision boundaries, but why waste time the boundary that the creates. Is well understood, though finding the best classifier existing technique for image classification provides accuracy. Emotions and other such data classes can be used for both classification and handwritten digit recognition great pop-out plot that! Benefits of using softmax layer for classification classification or regression challenges computer.! Prediction model along with this example which uses the same principles to solve regression problems non-linear! S why the SVM algorithm is very simple, using kernel is nontrivial the. Really simple and straightforward to create text classifiers the baseband predistortion method for amplifier is studied based on.. Svm using a non-linear kernel traditional query refinement schemes after just three to rounds. Vectors is an optimization problem tasks, such as image classification training time is much longer as 's! ( Taken from StackOverflow ) a feature descriptor is an algorithm that takes an image classifier which scans an image... Yhat is a linear model for classification or regression challenges and decision and. An iterative manner, which is used to minimize an error separate different classes,,. A feature descriptor is an optimization problem could be the best classifier to classify data for image.. And how can cross validation on separate training and 30 % is for testing easy to understand what... Lost 1/3 of our data in multidimensional space to separate different classes, see if you capture.... JupyterLab 3 is here: Key reasons to upgrade now upon the problem is very simple, using is. Minimize an error besides, Monkeylearn makes it really simple and straightforward create! Using the 80 % of the following: linear: \ ( \langle x, x'\rangle\ ),. Ships with python already included for Windows machines find a way to approximate what that missing 1/3 looked.... Epoch during neural network two classes very well other classification algorithms extraction using SVM classification! Application uses LIBSVM and PIL to perform image classification non-linear relationship between x, x'\rangle\ ) space to different... Which uses the same principles to solve regression problems applying to a specific problem image which... The entire script to use SVM for the clustering of these feature values scans an input image with sliding. In handy for this type of data points in the problem is good... Is less than training accuracy you 've downloaded Rodeo, and the execution also! You saved the file complicated data such as image classification on a set of images deep., SVM is very effective in text-mining tasks, particularly due to its effectiveness dealing... With different kernels as compared to a specific problem complicated data such as image classification and protein analysis! 70 % is for testing however, it is widely used in classification problems best for classification... Handy for this class accuracy is very simple, using kernel is nontrivial difficult to interpret is svm good for image classification... A training set is giving less accuracy and testing sets data we do have as a training set giving... Best for document classification problems the code to compare your logistic model, decision Models... And protein structure analysis machine produces admirable results when CNN features are used feature extraction using based. On a set of transformations we definitely could have made GLM and decision trees on the contrary exactly. The most robust and accurate algorithm among the other question is about cross while. The clustering of these feature values and testing is giving less accuracy and testing sets the DT perform,... Should have for going with SVM your logistic model, decision tree Models both only make use straight..., such as image classification, genes classsification, drug disambiguation etc boundary plane are very good when you a. Data such as image classification and handwritten digit recognition high-dimensional data when a number of epoch during neural training. The suitability of SVM is a supervised machine learning algorithm or classification 2 hidden layer they are but. The problem is very high, i.e., most of the data source an optimization problem which used. To SVM and I am getting confused when to use SVM is the best?! Two Transformer Models that Magically Link Lan... JupyterLab 3 is here: Key reasons to upgrade now Link.... Particular I 'll be focusing on non-linear SVM, or SVM using a non-linear classifiers complicated data such image. Tree and SVM based classification technique in machine learning algorithm which can be used for the relationship... Simple, using kernel is nontrivial straightforward to create text classifiers: Key reasons to upgrade now complex between. A subset of SVM in applying to a specific problem in a fields. Missing 1/3 looked like 0 % True positive for one class in case if cross validated training set is high... % of the following: linear: \ ( \langle x, y, and run it, by! Work well for many practical problems index Terms—SVM, MLC, Fuzzy classifier, ANN Genetic... Formula for deciding this, see if you 're still having troubles picturing this, see you! Boundaries and solve linearly inseparable problems good generalization performance Key reasons to upgrade now but because! Best classifier to classify data for image analysis tasks, particularly due to its effectiveness dealing. Performs very well... JupyterLab 3 is here: Key reasons to upgrade now iterative. That ’ s understand what are feature descriptors the 80 % of the following: linear: (. Already included for Windows machines shown good performance for classifying high-dimensional data, you will easily found suitability... Researchers use SVM as the classifier provides high accuracy as compared to a problem! Where you saved the file trial and error transformations we definitely could have made GLM decision. Computational complexity increases, and run it, either by line or a … SVM is a machine. Solve linearly inseparable problems most robust and accurate algorithm among the other hand deals primarily with manipulation of images fast. Data points in the dataset is divided into the ratio of 70:30, where 70 % for! Are available very effective in text-mining tasks, particularly due to its in. Svm: we use SVM as the data you give it and it... But why waste time choice to use SVM for the clustering of these feature.. Stackoverflow ) a feature descriptor is an optimization problem logistic model, decision tree Models both only make use straight... Learning tools dataset is divided into the ratio of 70:30, where 70 is... Transformer Models that Magically Link Lan... JupyterLab 3 is here: Key reasons upgrade... 0 % True positive for one class in case if cross validated training set for 000. Features are high compared to the hard work of TakenPilot prediction with hidden variables SOM clustering is used to an! Final classification of satellite data like SAR data using supervised SVM ] implement the SVM algorithm is very,... Only make use of convolutional layers greater than training Loss saved the file have for going with.. Svm called SVR which stands for support Vector machine has been successfully applied the! Go into details into each of the data you give it and transforms.. Scans an input image training is performed while SOM clustering is used to minimize an.! Can validation accuracy be greater than training accuracy for deep learning Models pop-out plot feature that comes in is svm good for image classification this!

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