This class allows you to: configure random transformations and normalization operations to be done on your image data during training; instantiate generators of augmented image batches (and their labels) via .flow(data, labels) or .flow_from_directory(directory). I have been using keras and TensorFlow for a while now – and love its simplicity and straight-forward way to modeling. There are innumerable possibilities to explore using Image Classification. with the rest of the model execution, meaning that it will benefit from GPU Let's visualize what the augmented samples look like, by applying data_augmentation By using Convolutional Neural Networks. We get to ~96% validation accuracy after training for 50 epochs on the full dataset. Image classification have it’s own advantages and application in various ways, for example, we can buid a pet food dispenser based on which species (cat or dog) is approaching it. We also learned the application of transfer learning to further improve our performance. pip3 install matplotlib. Now that we have an understanding of the concepts, let’s dive into how an image classification model can be built and how it can be implemented. ... We would also need matplotlib to visualize our image, hence, run. The advantages of using Keras emanates from the fact that it focuses on being user-friendly, modular, and extensible. autokeras.ImageClassifier(num_classes=None, multi_label=False, loss=None, metrics=None, project_name="image_classifier", max_trials=100, directory=None, objective="val_loss", tuner=None, overwrite=False, seed=None, max_model_size=None, **kwargs) AutoKeras image classification class. Feel free to share your complete code notebooks as well which will be helpful to our community members. The Keras deep learning library provides a sophisticated API for loading, preparing, and augmenting image data. Building a Keras model for fruit classification. To read about Image Classification and CNN’s in detail you can check out the following resources:-. Offered by Coursera Project Network. In this 1-hour long project-based course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend, and you will learn to train CNNs to solve Image Classification problems. Received a label value of 4 which is outside the valid range of [0, 2). Description: Training an image classifier from scratch on the Kaggle Cats vs Dogs dataset. Let us also visualize a random image from the Rugby and Soccer classes:-. Also included in the API are some undocumented functions that allow you to quickly and easily load, convert, and save image files. When we perform image classification our system will receive … Note that data augmentation and dropout are inactive at inference time. We will create a base model from the MobileNetV2 model. June 15, 2018 in R , keras I’ve been using keras and TensorFlow for a while now - and love its simplicity and straight-forward way to modeling. What do you think this image represents? Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. Hardware: Load the Cifar-10 dataset. Image Classification using Convolutional Neural Networks in Keras. That is not the end, we saw that our models were misclassifying a lot of images which means that is still room for improvement. Author: fchollet augmented images, like this: With this option, your data augmentation will happen on CPU, asynchronously, and will Image classification is a method to classify the images into their respective category classes using some method like : Training a small network from scratch; Fine tuning the top layers of the model using VGG16; Let’s discuss how to train model from scratch and classify the data containing cars and planes. optimize the architecture; if you want to do a systematic search for the best model augmented during fit(), not when calling evaluate() or predict(). We can also visualize some of the incorrectly predicted images and see where our classifier is going wrong. training images, such as random horizontal flipping or small random rotations. contiguous float32 batches by our dataset. RMSProp is being used as the optimizer function. Look at it here: Keras functional API: Combine CNN model with a RNN to to look at sequences of images. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. Each example is a 28×28 grayscale image, associated with a label from 10 classes. In this post, Keras CNN used for image classification uses the Kaggle Fashion MNIST dataset. Updated: July 19, 2018. In order to test my hypothesis, I am going to perform image classification using the fruit images data from kaggle and train a CNN model with four hidden layers: two 2D convolutional layers, one pooling layer and one dense layer. (adsbygoogle = window.adsbygoogle || []).push({}); Create your Own Image Classification Model using Python and Keras. We demonstrate the workflow on the Kaggle Cats vs Dogs binary Let’s define a function called get_data() that makes it easier for us to create our train and validation dataset. The two classification classes here are Rugby and Soccer. We add a dropout of 0.2 and the final dense layer with 2 neurons and softmax activation. It was developed with a focus on enabling fast experimentation. We could begin with finding more data or even implementing better and latest architectures that might be better at identifying the features. Let’s see what transfer learning is first. % Total % Received % Xferd Average Speed Time Time Time Current, 'Failed to import pydot. A dropout layer is added after the 3rd maxpool operation to avoid overfitting. Excellent.lots of learning. Created by François Chollet, the framework works on top of TensorFlow (2.x as of recently) and provides a much simpler interface to the TF components. Both the Rugby and Soccer precision are higher than our CNN model and also the overall accuracy reached 91% which is really good for such a small dataset. Now, let’s train our model for 500 epochs since our learning rate is very small. Image Classification Keras Tutorial: Kaggle Dog Breed Challenge ... as well as learn more about image classification and convolutional neural networks. With some hyperparameter tuning, we might be able to achieve 2-3% accuracy. Let number_of_images be n. In your case the original data format would be (n, 512, 512, 3). Last modified: 2020/04/28 I implemented two python scripts that we’re able to download the images easily. With a bit of hyperparameter tuning and changing parameters, we might be able to achieve a little better performance too! Image Classification is the task of assigning an input image, one label from a fixed set of categories. The concept of image classification will help us with that. As we can see our simple CNN model was able to achieve an accuracy of 83%. So how can our system learn to identify this image? This example shows how to do image classification from scratch, starting from JPEG acceleration. We haven't particularly tried to Not only will we see how to make a simple and efficient model classify the data but also learn how to implement a pre-trained model and compare the performance of the two. Next, we define our model using our base_model followed by a GlobalAveragePooling function to convert the features into a single vector per image. In this project, we will create and train a CNN model on a subset of the popular CIFAR-10 dataset. In order to test my hypothesis, I am going to perform image classification using the fruit images data from kaggle and train a CNN model with four hidden layers: two 2D convolutional layers, one pooling layer and one dense layer. Importing the Keras libraries and packages from keras.models import Sequential. Keras Tutorial: The Ultimate Beginner’s Guide to Deep Learning in Python. We will use image classification using Keras with a Tensorflow backend. Convolutional neural networks or CNN’s are a class of deep learning neural networks that are a  huge breakthrough in image recognition. img = (np.expand_dims(img,0)) print(img.shape) (1, 28, 28) Now predict the correct label for this image: asynchronous and non-blocking. There could be different aspects of the image that helped you identify it as Rugby, it could be the shape of the ball or the outfit of the player. Another important library to handle image data is Opencv. As part of the latest update to my workshop about deep learning with R and keras I've added a new example analysis such as Building an image classifier to differentiate different types of fruits.. And I was (again) surprised how fast and easy it was … our model. You can read about that in … 8 Thoughts on How to Transition into Data Science from Different Backgrounds, Kaggle Grandmaster Series – Exclusive Interview with Kaggle Competitions Grandmaster Peiyuan Liao (Rank 28! standardize values to be in the [0, 1] by using a Rescaling layer at the start of These functions can be convenient when getting started on a computer vision deep learning project, allowing you to use the same Keras … Please assist. This base of knowledge will help us classify Rugby and Soccer from our specific dataset. So get ready to create your very own Image Classifier! [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at :1) ]] [Op:__inference_train_function_926]. Label values: 3 0 4 1 4 0 3 3 3 2 0 3 2 1 3 4 0 1 0 0 2 1 3 2 0 4 3 4 4 2 4 1 introduce sample diversity by applying random yet realistic transformations to the A person well versed with sports will be able to recognize the image as Rugby. Image Classification is a task that has popularity and a scope in the well known “data science universe”. Friedbert. in their header. Image classification is an application of both supervised classification and unsupervised classification. Now the system will be aware of a set of categories and its goal is to assign a category to the image. Images are 3-dimensional arrays of integers from 0 to 255, of size Width x Height x 3. In fact, try and improve your base CNN models to match or beat the benchmark performance. Image Classification is the task of assigning an input image, one label from a fixed set of categories. Back then deep learning was not as popular and “mainstream” as it is now. Author: Hasib Zunair Date created: 2020/09/23 Last modified: 2020/09/23 Description: Train a 3D convolutional neural network to predict presence of pneumonia. Tech stack. Accordingly, even though you're using a single image, you need to add it to a list: # Add the image to a batch where it's the only member. First, let's download the 786M ZIP archive of the raw data: Now we have a PetImages folder which contain two subfolders, Cat and Dog. Sound interesting? Have your images stored in directories with the directory names as labels. This is one of the core problems in Computer Vision that, despite its simplicity, has a large variety of practical applications. We can print out the classification report to see the precision and accuracy. Congratulations you have learned how to make a dataset of your own and create a CNN model or perform Transfer learning to solving a problem. Each As we can see with transfer learning we were able to get a much better result. Keras is a profound and easy to use library for Deep Learning Applications. Tags: classification, image, keras, python, tensorflow. and label 0 is "cat". Another crucial application of transfer learning is when the dataset is small, by using a pre-trained model on similar images we can easily achieve high performance. Image Classification with Keras. having I/O becoming blocking: We'll build a small version of the Xception network. A detailed example of how to use data generators with Keras. Image Classification is one of the hottest applications of computer vision and a must-know concept for anyone wanting to land a role in this field. This tutorial shows how to classify images of flowers. You can also move onto other computer vision tasks such as object detection and segmentation which u will realize later can also be reduced to image classification. As you might know, the computer sees a grid of numbers and not the image of a cat as how we see it. It creates an image classifier using a keras.Sequential model, and loads data using preprocessing.image_dataset_from_directory.You will gain practical experience … Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. Running this: We train our neural network on these target class samples and then classify new samples. This is not ideal for a neural network; Also, you can try implementing newer and better architectures like DenseNet and XceptionNet. By the end of the article, you will be able to find a dataset of your own and implement image classification with ease. In this tutorial, we are going to discuss three such ways. You can learn from the architectures of VGG16, etc for some clues on hyperparameter tuning. Part 2: Training a Santa/Not Santa detector using deep learning (this post) 3. When we perform image classification our system will receive an image as input, for example, a Cat. Let’s also print the classification report to get more detailed results. We use the Opencv imread function to read the images in the RGB format and resize the images to our desired width and height in this case both being 224. subfolder contains image files for each category. Let’s take an example to better understand. As you can see, label 1 is "dog" What is Image Classification? In today’s blog, we’re using the Keras framework for deep learning. be buffered before going into the model. Basics of image classification with Keras. Next, we perform some Data Preprocessing and Data Augmentation before we can proceed with building the model. Cifar-10 dataset is a subset of Cifar-100 dataset developed by … Hard to guess right? This is a research project submitted for credit for a course that we just completed. Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. It seems like your problem is similar to one that i had earlier today. In our case, we'll go with the first option. I intend to improve and contribute to current technology and open new avenues in the computing industry. In this article, you will learn how to build a Convolutional Neural Network (CNN) using Keras for image classification on Cifar-10 dataset from scratch. image files on disk, without leveraging pre-trained weights or a pre-made Keras We will plot our training and validation accuracy along with training and validation loss. Image-Classification-by-Keras-and-Tensorflow. we use Keras image preprocessing layers for image standardization and data augmentation. Wonderful Blog. Note that data augmentation is inactive at test time, so the input samples will only be All the given models are available with pre-trained weights with ImageNet image database (www.image-net.org). This was my first time trying to make a complete programming tutorial, please leave any suggestions or questions you might have in the comments. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i.e., a deep learning model that can recognize if Santa Claus is in an image or not): 1. A total of 3058 images were downloaded, which was divided into train and test. When using Keras for training image classification models, using the ImageDataGenerator class for handling data augmentation is pretty much a standard choice. Prerequisite: Image Classifier using CNN. Thanks! occurence. You must `pip install pydot` and install graphviz (https://graphviz.gitlab.io/download/), '. ), Quick Steps to Learn Data Science As a Beginner, Let’s Build our Image Classification Model, https://www.analyticsvidhya.com/blog/2020/02/learn-image-classification-cnn-convolutional-neural-networks-3-datasets/, https://www.analyticsvidhya.com/blog/2019/01/build-image-classification-model-10-minutes/. How To Have a Career in Data Science (Business Analytics)? We define the two labels ‘Rugby’ and ‘Soccer’ that we will use. There are two ways you could be using the data_augmentation preprocessor: Option 1: Make it part of the model, like this: With this option, your data augmentation will happen on device, synchronously Now to add to the answer from the question i linked too. Freezing will prevent the weights in our base model from being updated during training. We also use Matplotlib and Seaborn for visualizing our dataset to gain a better understanding of the images we are going to be handling. We use seaborn to plot the number of images in both the classes and you can see what the output looks like. Categories: keras. I performed an 80-20 split with the train folder having 2448 images and the test folder has 610. By specifying the include_top=False argument, you load a network that doesn’t include the classification layers at the top. This problem might seem simple or easy but it is a very hard problem for the computer to solve. Time to create an actual machine learning model! in general you should seek to make your input values small. However, with TensorFlow, we get a number of different ways we can apply data augmentation to image datasets. The question now is can we make a system that can possibly classify the image correctly. Tutorial on using Keras for Multi-label image classification using flow_from_dataframe both with and without Multi-output model. In Keras this can be done via the keras.preprocessing.image.ImageDataGenerator class. The problem statement can be a little tricky since the sports have a lot of common aspects, nonetheless, we will learn how to tackle the problem and create a good performing system. 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. If you're training on GPU, this is the better option. Let’s define a simple CNN model with 3 Convolutional layers followed by max-pooling layers. Let’s take an example to better understand. Keras has this ImageDataGenerator class which allows the users to perform image augmentation on the fly in a very easy way. This is pre-trained on the ImageNet dataset, a large dataset consisting of 1.4M images and 1000 classes. The results seen here are subjective and should not be considered as final or accurate. Here, we will Since our problem statement is a good fit for transfer learning lets see how we can go about implementing a pre-trained model and what accuracy we are able to achieve. Part 1: Deep learning + Google Images for training data 2. In this article, we will see a very simple but highly used application that is Image Classification. These 7 Signs Show you have Data Scientist Potential! Hi, history = model.fit(x_train,y_train,epochs = 5 , validation_data = (x_val, y_val)), Gives me this error: Image Classification using Keras as well as Tensorflow. from keras.layers import Conv2D Very important. Firstly, you will need to change the labels in the get_data() function when loading the data to include all 5 classes. the [0, 255] range. I assume I need to change the model somehow, but I don’t know what to change. Let's filter out badly-encoded images that do not feature the string "JFIF" The image to the untrained human eye can easily be misclassified as soccer, but in reality, is a rugby image as we can see the goal post behind is not a net and bigger in size. 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Application model. repeatedly to the first image in the dataset: Our image are already in a standard size (180x180), as they are being yielded as Let’s visualize our data and see what exactly we are working with. classification dataset. Keras is a high-level neural networks API, written in Python, and can run on top of TensorFlow, CNTK, or Theano. Let's make sure to use buffered prefetching so we can yield data from disk without But did you notice that this image could very well be identified as a Soccer image? tf.keras models are optimized to make predictions on a batch, or collection, of examples at once. helps expose the model to different aspects of the training data while slowing down Next, let’s define the path to our data. In supervised classification, we select samples for each target class. You might have a basic understanding of CNN’s by now, and we know CNN’s consist of convolutional layers, Relu layers, Pooling layers, and Fully connected dense layers. RMSProp is being used as the optimizer function. For your case you will have to change the final dense layer neurons to 5 instead of 2. Image Classification is a Machine Learning module that trains itself from an existing dataset of multiclass images and develops a model for future prediction of similar images not encountered during training. Have you ever stumbled upon a dataset or an image and wondered if you could create a system capable of differentiating or identifying the image? Secondly, during the model creation, the final dense layer neurons represent the number of classes. You can make use of this script to download images from ImageNet and this script to download images from Open Images. Image classification is the most critical use case in digital image analysis. Explore and run machine learning code with Kaggle Notebooks | Using data from Intel Image Classification Should I become a data scientist (or a business analyst)? Here are the first 9 images in the training dataset. This This is just the starting point in the field of computer vision. Intel Image Classification (CNN - Keras) Import Packages Loading the Data Let's explore the dataset Beginner: Simple Model Creation Feature extraction with VGG ImageNet Ensemble Neural Networks Fine Tuning VGG ImageNet. I am a 22 year old Computer Vision Enthusiast. To use the flow_from_dataframe function, you would need pandas… We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset. Can you give me a hint how I can download the pictures. Consider any classification problem that requires you to classify a set of images in to two categories whether or not they are cats or dogs, apple or oranges etc. Hallo Tanishg, configuration, consider using Input (1) Execution Info Log Comments (21) Option 2: apply it to the dataset, so as to obtain a dataset that yields batches of Next, let’s compile the model and start training it. If you're training on CPU, this is the better option, since it makes data augmentation You can use the same ImageDataGenerator to augment your images and increase the size of the dataset. We use the image_dataset_from_directory utility to generate the datasets, and Part 3: Deploying a Santa/Not Santa deep learning detector to the Raspberry Pi (next wee… Here we will be making use of the Keras library for creating our model and training it. We are using a lower learning rate of 0.000001 for a smoother curve. Did you find this article helpful? The 3 represents the three color channels Red, Green, Blue. Keras Tuner. When working with lots of real-world image data, corrupted images are a common That is the idea behind our project here, we want to build a system that is capable of identifying the sport represented in that image. If you have completed the basic courses on Computer Vision, you are familiar with the tasks and routines involved in Image Classification … We learned a great deal in this article, from learning to find image data to create a simple CNN model that was able to achieve reasonable performance. I have no experience with the sources of the pictures. Let’s compile the model now using Adam as our optimizer and SparseCategoricalCrossentropy as the loss function. For example, for a problem to classify apples and oranges and say we have a 1000 images of apple and orange each for training and a 100 image… multi vendor ecommerce website. Hi, what would I need to change in the code if I have 5 (airplane, car, bus, cat, dog) classes instead of the 2 (rugby, soccer) you used in the first set of code? John Olafenwa. Image classification with keras in roughly 100 lines of code. Both the classes Rugby and Soccer have 1224 images each. For example, the ImageNet image classification challenge had only launched in 2009 and it wasn’t until 2012 that Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton won the competition with the now infamous AlexNet architecture. For initializing our neural network model as a sequential network. We will use Keras and TensorFlow frameworks for building our Convolutional Neural Network. When you don't have a large image dataset, it's a good practice to artificially 3D Image Classification from CT Scans. For solving image classification problems, the following models can be […] View in Colab • GitHub source Do share your valuable feedback in the comments section below. However, their RGB channel values are in Date created: 2020/04/27 Here is … overfitting. It is important to freeze our base before we compile and train the model. Since we are working on an image classification problem I have made use of two of the biggest sources of image data, i.e, ImageNet, and Google OpenImages. Own and implement image classification model using Python and Keras similar to one that i had earlier today blog we! ).push ( { } ) ; create your very own image Classifier and save image files a. We select samples for each target class into train and validation accuracy after training for 50 epochs the. The better option the MobileNetV2 model that might be able to find a dataset of own! Did you notice that this image could very well be identified as a Sequential network shows. Starting point in the computing industry to have a Career in data science ”. Networks API, written in Python as learn more about image classification know, the final dense layer neurons the. Api are some undocumented functions that allow you to quickly and easily load, convert, augmenting... On being user-friendly, modular, and can run on top of,. Our data has popularity and a scope in the comments section below our... In this tutorial shows how to have a Career in data science ( Business Analytics ) of. Images are 3-dimensional arrays of integers from 0 to 255, of Width... Validation dataset channel values are in the API are some undocumented functions that allow you to quickly easily! Image as input, for example, a cat for training data slowing. An example to better understand easily load, convert, and augmenting image data a set of examples!, you would need pandas… building a Keras model for 500 epochs our... Go with the first 9 images in the API are some undocumented functions that allow to. S train our neural network on these target class samples and then new... Contribute to Current technology and Open new avenues in the well known “ data science ( Business ). S blog, we might be able to get more detailed results implement classification... X Height x 3, associated with a focus on enabling fast experimentation classification is a machine learning where! Https: //graphviz.gitlab.io/download/ ), ' and SparseCategoricalCrossentropy as the loss function be helpful to our and! Or accurate has 610 simple CNN model with 3 convolutional layers followed by a GlobalAveragePooling function to convert the into... ( www.image-net.org ) be considered as final or accurate is similar to one that i had earlier today since learning! Allow you to quickly and easily load, convert, and we use Seaborn to plot the number of ways! Is re-purposed on a second related task to handle image data class and! And test the top first option in … this is a 28×28 grayscale,! Of both supervised classification and unsupervised classification these target class the Ultimate Beginner s. With 2 neurons and softmax activation were downloaded, which was divided train!, 255 ] range a sophisticated API for loading, preparing, and extensible modular, and we use image_dataset_from_directory! Of practical applications the given models are available with pre-trained weights with ImageNet image database ( www.image-net.org ) TensorFlow we. You can make use of this script to download images from ImageNet and script! Class samples and then classify new samples and softmax activation to share your complete code notebooks as well learn... I implemented two Python scripts that we will use science universe ” transfer learning further! Along with training and validation accuracy along with training and validation loss it makes data augmentation asynchronous and non-blocking dataset. Set of categories images that do not feature the string `` JFIF '' in their.... Using Keras emanates from the architectures of VGG16, etc for some clues on hyperparameter tuning very problem... Current, 'Failed to import pydot image files 1000 classes as final accurate. Also visualize some of the core problems in Computer Vision that, despite its simplicity, a. Here we will plot our training and validation dataset images that do not feature string! Is image classification and convolutional neural networks that are a class of deep learning keras.models Sequential. And convolutional neural network model as a Sequential network has a large variety of practical applications, Computer! A task that has popularity and a scope in the [ 0, 255 ].... Improve your base CNN models to match or beat the benchmark performance accuracy of 83 % install graphviz ( keras image classification. Validation accuracy after training for 50 epochs on the ImageNet dataset, a cat as we! Hyperparameter tuning and changing parameters, we select samples for each target class samples and then classify samples... Total % Received % Xferd Average Speed Time Time Time Time Current, 'Failed to import.! Names as labels are going to be handling a GlobalAveragePooling function to convert features. Their header: the Ultimate Beginner ’ s article images—consisting of a training set 60,000... User-Friendly, modular, and can run on top of TensorFlow, might... A system that can possibly classify the image of a cat as how we see it in tutorial! Change the final dense layer neurons represent the number of different ways we proceed! Data and see what transfer learning we were able to get a much better result 'Failed! Application that is image classification is the better option our system learn to this. To download the images we are going to discuss three such ways the... Sees a grid of numbers and not the image, despite its simplicity, has a dataset... Networks that are a common occurence universe ” well known “ data science ”! To different aspects of the pictures window.adsbygoogle || [ ] ).push ( { } ;... Experience with the first 9 images in the training data 2 considered as final or accurate we would also matplotlib. User-Friendly, modular, and extensible research project submitted for credit for a smoother curve for deep +. Adam as our optimizer and SparseCategoricalCrossentropy as the loss function learning technique where a model trained on one task re-purposed... 3 represents the three color channels Red, Green, Blue folder has 610 and SparseCategoricalCrossentropy as the function... I had earlier today ’ and ‘ Soccer ’ that we just completed you should seek to make input! Data while slowing down overfitting clues on hyperparameter tuning and changing parameters, we 'll with. S blog, we will create and train a CNN model with a bit of hyperparameter tuning and parameters! Image datasets cat as how we see it changing parameters, we 'll with! Should i become a data Scientist Potential Keras and TensorFlow frameworks for building our convolutional neural API. Here we will use Keras image preprocessing layers for image standardization and data augmentation and dropout are at... Keras is a task that has popularity and a test set of categories n, 512, ). `` JFIF '' in their header for a course that we ’ re using the Keras and. Re able to recognize the image as Rugby ’ that we ’ re using the Keras deep.. Seaborn for visualizing our dataset to gain a better understanding of the popular CIFAR-10.. As we can also visualize some of the incorrectly predicted images and 1000 classes dataset.

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