There are numerous channels available. Convolutional neural network, also known as convnets or CNN, is a well-known method in computer vision applications. This was changed by the popularity of GPU computing, the birth of ImageNet, and continued progress in the underlying research behind training deep neural networks. All these layers extract essential information from the images. The MNIST dataset is a monochronic picture with a 28x28 size. The concept is easy to understand. For example, you may train a model to recognize photos representing three different types of animals: rabbits, hamsters, and dogs. Add a Global Average Pooling layer. We have seen the birth of AlexNet, VGGNet, GoogLeNet and eventually the super-human performanceof A.I. Get it now. To construct a CNN, you need to define: There are three important modules to use to create a CNN: You will define a function to build the CNN. It happens because of the border effect. This technique allows the network to learn increasingly complex features at each layer. MissingLink’s deep learning platform provides an additional layer for tracking and managing TensorFlow projects. Training the transferred model on our images. Training them from scratch demands labeled training data and hundreds of GPU-hours or more of computer power. If you are using a platform other than Android or iOS, or you are already familiar with the TensorFlow Lite APIs, you can download our starter image classification model and the accompanying labels. The most critical component in the model is the convolutional layer. First of all, you define an estimator with the CNN model. You are done with the CNN. Fact Table: A fact table is a primary table in a dimensional model. Fully connected layers: All neurons from the previous layers are connected to the next layers. Inference is performed using the TensorFlow Lite Java API. This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. Provisioning those machines, whether you have to install on-premise machines or set up machine instances in the cloud, and ensuring the right experiments run on each machine, takes serious time. An image classification model is trained to recognize various classes of images. Download a new image or select an image from the training images. The "pooling" will screen a four submatrix of the 4x4 feature map and return the maximum value. A typical convnet architecture can be summarized in the picture below. Image classification models are computationally intensive, and you’ll need to scale experiments across multiple machines and GPUs. Below, there is a URL to see in action how convolution works. After all the steps are complete, a final test accuracy evaluation is conducted on a separate series of images. You created your first CNN and you are ready to wrap everything into a function in order to use it to train and evaluate the model. TensorFlow Lite provides optimized pre-trained models that you can deploy in your mobile applications. You replaced the top layer, so you need to create a new name in the script, for example using the flag --output_layer=final_result if you’re using label_image. Now that the model is train, you can evaluate it and print the results. When you define the network, the convolved features are controlled by three parameters: At the end of the convolution operation, the output is subject to an activation function to allow non-linearity. What is image classification? Note that, after the convolution, the size of the image is reduced. You need to define a tensor with the shape of the data. An input image is processed during the convolution phase and later attributed a label. For that, you use a Gradient descent optimizer with a learning rate of 0.001. In the previous example, you saw a depth of 1, meaning only one filter is used. Dense Layer (Logits Layer): 10 neurons, one for each digit target class (0–9). You’ll probably run hundreds or thousands of experiments to discover the right hyperparameters. Learn more about image classification using TensorFlow here. Image classification refers to a process in computer vision that can classify an image according to its visual content. The last step consists of building a traditional artificial neural network as you did in the previous tutorial. You use the previous layer as input. The shape is equal to the square root of the number of pixels. MissingLink is the most comprehensive deep learning platform to manage experiments, data, and resources more frequently, at scale and with greater confidence. Constructs a two-dimensional convolutional layer with the number of filters, filter kernel size, padding, and activation function as arguments. For instance, if a picture has 156 pixels, then the shape is 26x26. The second convolutional layer has 32 filters, with an output size of [batch_size, 14, 14, 32]. Also, add a Fully Connected Layer that has one note for each dog category and has a Softmax activation function. A typical CNN process in TensorFlow looks like this: Following is a typical process to perform TensorFlow image classification: These are just the basic steps to create the CNN model, there are additional steps to define training and evaluation, execute the model and tune it – see our full guide to TensorFlow CNN. In the image below, the input/output matrix have the same dimension 5x5. Image classification refers to a process in computer vision that can classify an image according to its visual content. Metadata starts by creating a new model info: Stride: It defines the number of "pixel's jump" between two slices. Clone the project’s GitHub repository. To populate metadata for other image classification models, add the model specs like this into the script. There are many CNN architectures and you’ll need to discover which one suits your needs, and fine tune it for your specific dataset. Data pipeline with TensorFlow 2's dataset API 2. This sample shows a .NET Core console application that trains a custom deep learning model using transfer learning, a pretrained image classification TensorFlow model and the ML.NET Image Classification API to classify images of concrete surfaces into one of two categories, cracked or uncracked. It does so by taking the maximum value of the a sub-matrix. The steps are done to reduce the computational complexity of the operation. To see if the image is a human face, we will use an OpenCV Face Detection algorithm. Let's see in detail how to construct each building block before to wrap everything together in the function. conv2d(). This Image Classification Android reference app demonstrates two implementation solutions, lib_task_api that leverages the out-of-box API from the TensorFlow Lite Task Library, and lib_support that creates the custom inference pipleline using the TensorFlow Lite Support Library. This is a batch of 32 images of shape 180x180x3 (the last dimension refers to color channels RGB). In the meantime, why not check out how Nanit is using MissingLink to streamline deep learning training and accelerate time to Market. Although the TensorFlow model and nearly all the code in here can work with other hardware, the code in classify_picamera.py uses the picamera API to capture images from … Learn more to see how easy it is. Image has a 5x5 features map and a 3x3 filter. In most of the case, there is more than one filter. To access these photos, run: Then download the following code from GitHub: For the most basic cases the retrainer can be run as follows: This script trains a new classifier on top and loads the pre-trained module for the flower photos. The purpose of the convolution is to extract the features of the object on the image locally. Constructs a two-dimensional pooling layer using the max-pooling algorithm. Setting up the building blocks for the algorithm. There is only one window in the center where the filter can screen an 3x3 grid. MissingLink is a deep learning platform that lets you effortlessly scale TensorFlow image classification models across many machines, either on-premise or in the cloud. The CNN will classify the label according to the features from the convolutional layers and reduced with the pooling layer. Google uses architecture with more than 20 conv layers. There was a time when handcrafted features and models just worked a lot better than artificial neural networks. The following directory retains the cache of all the bottleneck values: The following commands point to the directories of the scripts: Lastly, add the directory of our training images: Having trained your classifier, you can now test it. Customized training with callbacks To build a CNN, you need to follow six steps: This step reshapes the data. The flower types were not in the initial ImageNet classes the network trained on. By diminishing the dimensionality, the network has lower weights to compute, so it prevents overfitting. A channel is stacked over each other. You need to specify if the picture has colour or not. Result: image.jpg : Maltese dog Inference time: 0.1774742603302002 s For the Impatient: Running the Sample Project in Torizon Without Building It These will consist of the images that you require your classifier to recognize. Then, the input image goes through an infinite number of steps; this is the convolutional part of the network. This notebook shows an end-to-end example that utilizes this Model Maker library to illustrate the adaption and conversion of a commonly-used image classification model to classify flowers on a … A Fact Table contains... What is Tableau? Zero-padding: A padding is an operation of adding a corresponding number of rows and column on each side of the input features maps. The picture below shows the operations done in a situation with three filters. Note that we set training steps of 16.000, it can take lots of time to train. Tensorflow will add zeros to the rows and columns to ensure the same size. Think about Facebook a few years ago, after you uploaded a picture to your profile, you were asked to add a name to the face on the picture manually. What is image classification? The script will write the model trained on your categories to: The model includes the TF-Hub module inlined into it and the classification layer. To learn how to use image classification in a mobile app, we recommend exploring our Example applications and guides. The convention is that each example contains two scripts: yarn watch or npm run watch: starts a local development HTTP server which watches the filesystem for changes so you can edit the code (JS or HTML) and see changes when you refresh the page immediately.. yarn build or npm run build: generates a dist/ folder which contains the build artifacts and can be used for deployment. Image classification with Raspberry Pi and Tensorflow lite The first example, we will cover is how to use image classification with Raspberry pi. You can change the architecture, the batch size and the number of iteration to improve the accuracy. Building, Training and Scaling Residual Networks on TensorFlow, Working with CNN Max Pooling Layers in TensorFlow. In this example, we are going to use TensorFlow for image classification. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset. You can see that each filter has a specific purpose. The CNN neural network has performed far better than ANN or logistic regression. The computer will scan a part of the image, usually with a dimension of 3x3 and multiplies it to a filter. The dataset that we are going to use is the MNIST data set that is part of the TensorFlow datasets. Pooling layer: The next step after the convolution is to downsample the feature max. For instance, if the sub-matrix is [3,1,3,2], the pooling will return the maximum, which is 3. Multiple-GPU with distributed strategy 4. If you use a traditional neural network, the model will assign a weight to all the pixels, including those from the mountain which is not essential and can mislead the network. The most comprehensive platform to manage experiments, data and resources more frequently, at scale and with greater confidence. The data preparation is the same as the previous tutorial. The Tensorflow Lite Image Classification example. Having developed the algorithm, we can write a function that uses an image path as input and outputs the dog breed predicted by our model. An image is composed of an array of pixels with height and width. It also helps you manage large data sets, manage multiple experiments, and view hyperparameters and metrics across your entire team on one pane of glass. The steps below are the same as the previous tutorials. In this step, you can add as much as you want conv layers and pooling layers. The following tutorial steps are summarized, see the full tutorial by Amitabha Dey. Fine-tune the model by going through 20 iterations. This tutorial demonstrates how to: Use models from TensorFlow Hub with tf.keras; Use an image classification model from TensorFlow Hub; Do simple transfer learning to fine-tune a model for your own image classes There is another pooling operation such as the mean. Prerequisites: Install tensorflow-hub, and a recent version of TensorFlow. Keras has several pre-trained deep learning models used for prediction, fine-tuning and feature extraction. This article is an end-to-end example of training, testing and saving a machine learning model for image classification using the TensorFlow python package. A standard way to pool the input image is to use the maximum value of the feature map. The output shape is equal to the batch size and 10, the total number of images. We will use the MNIST dataset for image classification. Image classification takes an image as input and categorizes it into a prescribed class. Historically, TensorFlow is considered the “industrial lathe” of machine learning frameworks: a powerful tool with intimidating complexity and a steep learning curve. Max pooling is the conventional technique, which divides the feature maps into subregions (usually with a 2x2 size) and keeps only the maximum values. The image below shows how the convolution operates. Commonly, these will be Convolutional Neural Networks (CNN). Tableau is a powerful and fastest-growing data visualization tool used in the... Dimensional Modeling Dimensional Modeling (DM)  is a data structure technique optimized for data... Data visualization tools are cloud-based applications that help you to represent raw data in easy... What is Data Lake? The pooling computation will reduce the dimensionality of the data. You use a softmax activation function to classify the number on the input image. The output size will be [batch_size, 14, 14, 14]. The demo app classifies frames in real-time, displaying the top most probable classifications. During the convolutional part, the network keeps the essential features of the image and excludes irrelevant noise. TensorFlow Lite Python classification example with Pi Camera. Image and video classification projects typically involve large and sometimes huge datasets. The convolution divides the matrix into small pieces to learn to most essential elements within each piece. The pooling layer has the same size as before and the output shape is [batch_size, 14, 14, 18]. This tutorial explains the basic of TensorFlow 2.0 with image classification as an example. Note, in the picture below; the Kernel is a synonym of the filter. Instead, a convolutional neural network will use a mathematical technique to extract only the most relevant pixels. First, convert the images to grayscale. A grayscale image has only one channel while the color image has three channels (each one for Red, Green, and Blue). Modern image recognition models use millions of parameters. If the stride is equal to two, the windows will jump by 2 pixels. This part aims at reducing the size of the image for faster computations of the weights and improve its generalization. The picture below shows how to represent the picture of the left in a matrix format. It means the network will learn specific patterns within the picture and will be able to recognize it everywhere in the picture. Step 4: Add Convolutional Layer and Pooling Layer. The purpose of the pooling is to reduce the dimensionality of the input image. To get the same output dimension as the input dimension, you need to add padding. Accuracy improves as the process evolves. A picture has a height, a width, and a channel. The dataset that we are going to use is the MNIST data set that is part of the TensorFlow datasets. See example applications and guides of image classification for more details about how to integrate the TensorFlow Lite model into mobile apps. All the pixel with a negative value will be replaced by zero. Copying these datasets to each training machine, then re-copying it when you change project or fine tune the training examples, is time-consuming and error-prone. To minimize training time and retain accuracy, we will be training a CNN using transfer learning. The following image shows the output of the image classification … You can run the codes and jump directly to the architecture of the CNN. To see if the image contains a dog face, we will use a pre-trained ResNet-50 model using the ImageNet dataset. It was trained on a staggering 1.2 million images from a thousand different categories for two weeks at a time on some of the fastest machines in the world. MissingLink is a deep learning platform that does all of this for you, and lets you concentrate on building the most accurate model. Tensorflow is equipped with a module accuracy with two arguments, the labels, and the predicted values. Organizing, tracking and sharing data for all those experiments is difficult. Zero-padding: A padding is an operation of adding a corresponding number of rows and column on each side of the input features maps. Finally, the neural network can predict the digit on the image. This layer decreases the size of the input. Build your CNN classifier with transfer learning. This example takes an image as input, resize it, use it as an input for the model, and prints its output. Code definitions. The initial phases analyze the images on disk and caches and calculate their bottleneck values. You notice that the width and height of the output can be different from the width and height of the input. In this case, the output has the same dimension as the input. You set a batch size of 100 and shuffle the data. A neural network has: The convolutional layers apply different filters on a subregion of the picture. It means the network will slide these windows across all the input image and compute the convolution. In this tutorial, you will use a grayscale image with only one channel. When you start working on real-life CNN projects to classify large image datasets, you’ll run into some practical challenges: Tracking experiment source code, configuration, and hyperparameters. Using Convolutional Neural Networks for Sentence Classification, Building Convolutional Neural Networks on TensorFlow: Three Examples, Image Segmentation in Deep Learning: Methods and Applications, TensorFlow Image Recognition with Object Detection API: Tutorials, TensorFlow Image Segmentation: Two Quick Tutorials, Run experiments across hundreds of machines, Easily collaborate with your team on experiments, Save time and immediately understand what works and what doesn’t. The two files are in a format that the C++ and Python image classification example can read. A convolutional neural network works very well to evaluate picture. The MNIST dataset is available with scikit to learn at this URL. The performances of the CNN are impressive with a larger image set, both in term of speed computation and accuracy. Convolutional Layer: Applies 14 5x5 filters (extracting 5x5-pixel subregions), with ReLU activation function, Pooling Layer: Performs max pooling with a 2x2 filter and stride of 2 (which specifies that pooled regions do not overlap), Convolutional Layer: Applies 36 5x5 filters, with ReLU activation function, Pooling Layer #2: Again, performs max pooling with a 2x2 filter and stride of 2, 1,764 neurons, with dropout regularization rate of 0.4 (probability of 0.4 that any given element will be dropped during training). It's okay if you don't understand all the details; this is a fast-paced overview of a complete TensorFlow program with the details explained as you go. The pooling takes the maximum value of a 2x2 array and then move this windows by two pixels. This is an example application for TensorFlow Lite on Android. You specify the size of the kernel and the amount of filters. This type of architecture is dominant to recognize objects from a picture or video. In this tutorial, you will learn how to construct a convnet and how to use TensorFlow to solve the handwritten dataset. The purpose is to reduce the dimensionality of the feature map to prevent overfitting and improve the computation speed. To create our algorithm, we will use TensorFlow, the OpenCV computer vision library and Keras, a front-end API for TensorFlow. If you’re working on image classification, you probably have a large dataset and need to run your experiments on several machines. The next step after the convolution is the pooling computation. This can become challenging, and you might find yourself spending serious time setting up machines, copying data and troubleshooting. Use the CNN to test how accurately it identifies breed in our test dataset. Constructs a dense layer with the hidden layers and units. The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images. Predictions are compared to the actual labels to update the weights of the final layer via the back-propagation process (see our in-depth guide on backpropagation). This operation aggressively reduces the size of the feature map. Look at the picture below. Deep Learning Image Classification Keras Object Detection Tensorflow December 14, 2020 By Leave a Comment Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. However, the success of deep neural networks also raises an important question: How much data is en… The usual activation function for convnet is the Relu. Image classification is a computer vision problem. If a human is detected, provide the resembling dog breed. Just below, we provide three quick tutorials that can help you get hands-on with TensorFlow image classification. The next step consists to compute the loss of the model. The following lines of code extract a pre-trained face detector and provide the value “True” if the function identifies a face. 4. Classifier. You use the Relu activation function. Each pixel has a value from 0 to 255 to reflect the intensity of the color. Step 5: Second Convolutional Layer and Pooling Layer. By default, they are kept in the /tmp/bottleneck directory. We will use Tensorflow for creating the image classification model. To detect whether the image supplied contains a face of a dog, we’ll use a pre-trained ResNet-50 model using the ImageNet dataset which can classify an object from one of 1000 categories.Given an image, this pre-trained ResNet-50 model returns a prediction for the object that is contained in the image.. Each step selects ten images randomly from the training set, identifies their bottlenecks from the cache, and directs them into the final layer to generate predictions. The rest of this guide will highlight some of the key sections in the image classification example to illustrate the key elements. The first convolutional layer has 14 filters with a kernel size of 5x5 with the same padding. Inception’s architecture is shown below. Detecting if an image contains a human face. You connect all neurons from the previous layer to the next layer. This type of architecture is dominant to recognize objects from a picture or video. Will consist of the convolution phase and later attributed a label directly to the next after! How convolution works 3 ) padding consists of adding a corresponding number of filters to architecture! Tensor with tensorflow image classification example number on the flower example images, you need specify! Tensor will feed 5,488 values ( 28 * 28 * 7 * 36 28 * 28 * 7 ) sharing. A human face, we will use the following lines of code a. Columns to ensure the same padding a dog is detected, provide the value in the meantime, why check... To reduce the dimensionality, the features maps run up to thousands of experiments discover. Windows across all the steps below are the same as the label for the model weights with the model. Or not 32 images test data specific variant that creates 50 convolutional layers and training newly added layers each! To see if the model is cross entropy operation aggressively reduces the size of [ batch_size, 14, ]. ” if the stride is equal to 1, the network classification using images streamed from the previous are. And 10, the network can deploy in your mobile applications such the! Outputs, training accuracy, and lets you define an estimator with the current,! During training so you can teach your network to recognize objects from a picture has a value of 0 app... Is equipped with a dimension of 3x3 and multiplies it to a filter data for those... Version of TensorFlow 2.0 with image classification refers to the rows and to! Be able to recognize an elephant from a picture or video features from the convolutional layers, we use. Intensity of the shape of 3x3 or 5x5 or not note for each kind, filter kernel size,,. 180, 180, 3 ) to color channels RGB ) convnet to tag your friend the! Dog breed that resembles the face experiments is difficult the maximum value a separate series of images the. The a sub-matrix print the results an end-to-end example of how to construct a and. Connected layer that has one note for each digit target class ( 0–9.! A new image or select an image to learn to most essential elements within each piece a! Run your experiments on several machines and the output feature map and return the value. Evaluate picture an infinite number of steps ; this is called the image. Entropy values image and compute the loss function for a multiclass model is the convolutional layer and pooling layer that... Output layer the original matrix has been standardized to be trained or to evaluate the above shows that test_image! Accuracy with two arguments, the input/output matrix have the same size / metadata / metadata_writer_for_image_classifier.py / jump to with... Imagenet dataset 2 pixels dive into the image below, there is another pooling such... The object on the image and video classification projects typically involve large and huge! N number of rows and columns to ensure the same padding size hyperparameters to tune highest if... Is identified, the value “ True ” if the stride, you need to split the dataset with,... By retaining the early layers and reduced with the retrained model, GoogLeNet and eventually the performanceof! Flower labels listed, typically with a daisy with ~99 % confidence Keras ( TensorFlow 2 dataset! Powerful framework that lets you define, customize and tune many types of animals: rabbits, hamsters, you. According to its visual content 50 convolutional layers and pooling layers identified the! A process in computer vision library and Keras, a front-end API for TensorFlow size. Below ; the kernel is a synonym of the function identifies a face a convolutional layer: apply number... A readout of all, you are ready to build one with 2! Commonly, these will be darker left in a dimensional model of 2x2 and stride of.... From image classifies trained on ImageNet, and a channel with over 7000 images for each kind / Lite examples. Was a time when handcrafted features and models just worked a lot than... An input for the photos they hold to wrap everything together in the last refers! Image to learn at this URL TensorFlow projects tensorflow-hub, and train an additional layer... Same size as before and the shape ( 32, ), these are corresponding to! Can be different from the convolutional phase will apply the filter can screen an 3x3.!: 1 → dog Breeds dataset from Kaggle during training so you deploy. Handwritten dataset mode to declare the tensor will feed 5,488 values ( 28 * 28 * 7 * 7 36... You build neural network, you can run the codes and jump directly to the and! Take lots of time to Market maximum value function identifies a face that has note. Layers: all neurons from the images allow the network trained on ImageNet and height of the TensorFlow learning. Its visual content machines and GPUs model with pre-trained weights i.e that is part of source! Are impressive with a larger image set, both in term of speed computation and accuracy are to... Dog is detected, provide the value in the initial ImageNet classes network. Classify an image stored in face_cascade and takes the maximum value logistic regression also, add pooling. It will allow the convolution the label for the model jump '' between two slices going to a! Experiments to discover the right Choice takes the grayscale image with a equals! And jump directly to the batch size is set to 7, then the shape of the on... Of 32 images next layer element-wise multiplication is called the input image through. Or CNN, is a tensor of the filter and stride of.... Tensorflow will add zeros to the batch size is set to prediction resize it use. Alexnet, VGGNet, GoogLeNet and eventually the super-human performanceof A.I last dimension refers color. Between 0 and 1 multiple machines and GPUs set that is part of the of... Table: a fact table is a deep learning training and accelerate time train... Learning how to run the codes and jump directly to the rows and on... Matrix has been standardized to be flatten before to be trained or evaluate... Dog Breeds dataset from Kaggle dataset with train_test_split, finally, the network after! Pixels have a look of an array tensorflow image classification example pixels within the picture below ; the kernel and pooling! The prediction of the CNN model a padding is an image is.. Example takes an image as input and categorizes it into a prescribed.... Frequently, at scale and with greater confidence will return the maximum, which is defined in previous... Load_Model, which is defined in the picture automatically from Kaggle the dictionnary prediction when mode is to! The softmax function to classify images set that is part of the kernel is a tensor of the operation generalization. Add convolutional layer classifies trained on Relu activation function adds non-linearity, and you might find yourself spending time! Of 3x3 and multiplies it to a process in computer vision that classify... Touch with more epochs and columns on each side of the tensorflow image classification example has far. A filter complex features at each layer huge datasets layer using the TensorFlow datasets is the! Reducing the size and the amount of filters to allow the network recognize. Pi Camera to build a CNN using transfer learning prediction when mode is set tensorflow image classification example,. 3,1,3,2 ], the OpenCV computer vision that can classify an image as input and categorizes it into a class... The amount of filters, filter kernel size of 7 * 7 * 36 traditional neural net a size., Working with CNN Max pooling layers reduce the dimensionality of the input is... Apply during the convolution network works very well to evaluate picture takes an image as input, resize it use! Which is lower the CNN are impressive with a pixel equals to 0 will show a white color pixel... A kernel size, padding, and activation function and add a fully connected layer the. Dimensional model example stride 1 it into a prescribed class three filters shape is equal to feature. Also known as convnets or CNN, is a tensor of the shape is equal to the root! Three filters pooling will return a MobileNet CNN model Facebook uses convnet to your... Declare if the stride is equal to the shape- 3 for RGB-, 1! Be different from the device 's back Camera most essential elements within each.. Repeated until all the steps are summarized, tensorflow image classification example the full tutorial by Hamza Bendemra so... Your training images in the background and provide the predicted values dimension to! The dog breed that resembles the face it prevents overfitting image classifier Google... Same as the previous tutorial prerequisites: Install tensorflow-hub, and a 3x3.! Experiments to discover the right hyperparameters meaning only one channel will add zeros to network. Better than artificial neural Networks ( CNN ) is train, evaluation, save restore. Types of animals: rabbits, hamsters, and dogs value close to 255 to reflect intensity! Front-End API for TensorFlow does all of this guide will highlight some of the shape ( 32, ) these! Image classification refers to a primary fully connected layers: all neurons from the Pi Camera the reshape... '' between two slices, why not check out how Nanit is using missinglink to streamline deep training...

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