Training data was also shuffled during training the model, while validation data was used to get the validation accuracy and validation loss during training. The normalized confusion matrix plot of the predictions on the validation set is given here. A well-designed convolutional neural network should be able to beat the random choice baseline model easily considering even the KNN model clearly surpasses the initial benchmark. The images are histopathologic… However their histograms are quite similar. Training data set would contain 85–90% of the total labeled data. Computer vision and neural networks are the hot new IT of machine learning techniques. The cell blocks below will accomplish that: The first def function is letting our machine know that it has to load the image, change the size and convert it to an array. Kaggle Notebooks come with popular data science packages like TensorFlow and PyTorch pre-installed in Docker containers (see the Python image GitHub repo) that run on Google Compute Engine VMs. Participants of similar image classification challenges in Kaggle such as Diabetic Retinopathy , Right Whale detection (which is also a marine dataset) has also used transfer learning successfully. For each experiment only the best model was saved along with their weights(a model only gets saved per epoch if it shows higher validation accuracy than the previous epoch ). If I could train the data augmented model for a few more epochs it’d probably yield even better results. However, this is not the only method of checking how well our machines performed. This article explains the basics of multiclass image classification and how to perform image augmentation. Fortune report on current usage of artificial intelligence in fishing industry, The Nature Conservancy Fishery Monitoring, http://www.exegetic.biz/blog/wp-content/uploads/2015/12/log-loss-curve.png, http://cs231n.github.io/transfer-learning/, https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html, Building a Credit Card Recommender and deploying on web and Chatbot Platform, Question Answering with Pretrained Transformers Using Pytorch, The 10 best new features in Scikit-Learn 0.24 , Natural Language Generation (Practical Guide), Keystroke Dynamics Analysis and Prediction — Part 1 (EDA), Predicting House Prices with Machine Learning. Here is what I did. The classification accuracies of the VGG-19 model will be visualized using the … A more realistic example of image classification would be Facebook tagging algorithm. With data augmentation, each epoch with only 3777 training images takes me around 1 hour on my laptop, training on 8000 images would likely take 2.5x the time where each of the batches would even be slightly altered by keras when I’m using data augmentation, which takes some more time. In addition, butterflies was also misclassified as spiders because of probably the same reason. Here’s the accuracy/loss graph of the model with batch normalization, but without data augmentation. This goal of the competition was to use biological microscopy data to develop a model that identifies replicates. There are many transfer learning model. I’d have had to resize for feeding them into CNN in any case, however, resizing also was important to avoid data leakage issues. The pretrained model is available in Caffe, Torch, Keras, Tensorflow and many other popular DL libraries for public use. Images are not guaranteed to be of fixed dimensions and the fish photos are taken from different angles. It is also best for loss to be categorical crossenthropy but everything else in model.compile can be changed. The leaderboard log-loss is 1.19, so the log-loss is quite close. There are so many things we can do using computer vision algorithms: 1. The data is news data and labels (classes) are the degree of news popularity. Both elephants and horses are rather big animals, so their pixel distribution may have been similar. This sort of problems can probably be overcome by adding more data for the other classes, either via data augmentation or by collecting real video footage again. Additionally, batch normalization can be interpreted as doing preprocessing at every layer of the network, but integrated into the network itself. But thankfully since you only need to convert the image pixels to numbers only once, you only have to do the next step for each training, validation and testing only once- unless you have deleted or corrupted the bottleneck file. Here each image has been labeled with one true class and for each image a set of predicted probabilities should be submitted. Initially the baselines with random choice and K-nearest neighbors were implemented for comparison. For neural networks, this is a key step. Random choice : We predict equal probability for a fish to belong to any class of the eight classes for the naive benchmark. Obvious suspects are image classification and text classification, where a document can have multiple topics. There are lots on online tutorial on how to make great confusion matrix. The only important code functionality there would be the ‘if normalize’ line as it standardizes the data. Creating a bottleneck file for the training data. In the above equation, if the class label is 1(the instance is from that class) and the predicted probability is near to 1(classifier predictions are correct), then the loss is really low as log(x) → 0 as x →1 , so this instance contributes a small amount of loss to the total loss and if this occurs for every single instance(the classifiers is accurate) then the total loss will also approach 0. Making an image classification model was a good start, but I wanted to expand my horizons to take on a more challenging tas… Transfer learning is handy because it comes with pre-made neural networks and other necessary components that we would otherwise have to create. Graphically[¹] , assuming the ith instance belongs to class j and Yij= 1 , it’s shown that when the predicted probability approaches 0, loss can be very large. On the other hand, if the class label is 1(the instance is from that class) and the predicted probability is close to 0(the classifier is confident in its mistake), as log(0) is undefined it approaches ∞ so theoretically the loss can approach infinity. When you upload an album with people in them and tag them in Facebook, the tag algorithm breaks down the person’s picture pixel location and store it in the database. Jupyter is taking a big overhaul in Visual Studio Code. And that is the summary of the capstone project of my Udacity Machine Learning Nanodegree. Finally, we define the epoch and batch sizes for our machine. In this tutorial, you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems. This is importing the transfer learning aspect of the convolutional neural network. In this dataset input images also come in different sizes and resolutions, so they were resized to 150 x 150 x 3 to reduce size.Dataset given by Kaggle does not have any validation set, so it was split into a training set and a validation set for evaluation. The Nature Conservancy Fishery Monitoring competition has attracted the attention of the contestants and have been featured in publications such as Engadget ,Guardian and Fortune. Histograms represent the color distribution of an image by plotting the frequencies of each pixel values in the 3 color channels. Now to make a confusion matrix. For this part, I will not post a picture so you can find out your own results. Here weights from a convolutional neural network pretrained on imagenet dataset is finetuned to classify fishes. Then we simply tell our program where each images are located in our storage so the machine knows where is what. Validation data set would contain 5–10% of the total labeled data. This will be used to convert all image pixels in to their number (numpy array) correspondent and store it in our storage system. Because each picture has its own unique pixel location, it is relatively easy for the algorithm to realize who is who based on previous pictures located in the database. The second cell block takes in the converted code and run it through the built in classification metrics to give us a neat result. Perhaps, the fishing boats should make some area in their boats as a reference point too for faster classification. Ask Question Asked 3 years, 2 months ago. Please note that unless you manually label your classes here, you will get 0–5 as the classes instead of the animals. As we can see in our standardized data, our machine is pretty good at classifying which animal is what. Image Scene Classification of Multiclass. Object tracking (in real-time), and a whole lot more.This got me thinking – what can we do if there are multiple object categories in an image? I got the code for dog/cat image classification and I compiled and ran and got 80% accuracy. In this project, transfer learning along with data augmentation will be used to train a convolutional neural network to classify images of fish to their respective classes. Training with too little epoch can lead to underfitting the data and too many will lead to overfitting the data. Take a look. Let’s import all the necessary libraries first: In this step, we are defining the dimensions of the image. The model was built with Convolutional Neural Network (CNN) and Word Embeddings on Tensorflow. This step is fully customizable to what you want. I didn’t do it this time because with 8 class the training set would be around 8000 images. Activation layers apply a non-linear operation to the output of the other layers such as convolutional layers or dense layers. Accuracy is the second number. If you don’t have Kaggle account, please register one at Kaggle. Our engineers maintain these Docker images so that our users don’t need to worry about installation and dependency management, a huge barrier to getting started with data science. 2. Note that instead of using train_test_split methods in scikit-learn I randomly took 0.8% of each classes from the training set to the validation set while preserving the directory structure. The final phase is testing on images. Data Augmentation : Data augmentation is a regularization technique where we produce more images from the training data provided with random jitter, crop, rotate, reflect, scaling etc to change the pixels while keeping the labels intact. I particularly like VGG16 as it uses only 11 convolutional layers and pretty easy to work with. This model beats the K-nearest benchmark by 27.46% decrease and the random choice model by 50.45% decrease of multi-class log-loss. This means that the tagging algorithm is capable of learning based on our input and make better classifications in the future. My fully connected model on CNN features yielded a 3.10 score only, even if it had the same structure as original VGG-16’s fully connected model except with more dropout. Batch can be explained as taking in small amounts, train and take some more. VGG16 architecture diagram without the fully connected layer is given below. Kaggle Competition: Product Classification Machine Learning CS933 Term Project Name: Muping He Jianan Duan Sinian Zheng Acknowledgements : These are the complete, official rules for the Competition (the 'Competition Rules') and incorporate by reference the contents of the Competition Website listed above. The validation curve most likely will converge to the training curve over sufficient number of epochs. Multiclass log-loss punishes the classifiers which are confident about an incorrect prediction. Since its a image classification contest where the categories are not strictly taken from the imagenet categories(e.g cats and dogs), and the domain is very novel and practical, I believe it’s a decent score. the files to the tensor format step by step. The important factors here are precision and f1-score. However, illegal fishing remains a threat for the marine ecosystem in these regions as fishermen often engage in overfishing and catching of protected species for deep-sea tourism such as shark and turtles. To visualize, here is the final model’s accuracy/loss chart over 5 epochs. This is why before extracting the convolutional features for transfer learning, I created a basic CNN model to experiment with the parameters. Finetuning refers to the process of training the last few or more layers of the pretrained network on the new dataset to adjust the weight. The metric used for this Kaggle competition is multi-class logarithmic loss (also known as categorical cross entropy). In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. Chickens were misclassified as butterflies most likely due to the many different types of pattern on butterflies. Here we calculate the histograms for each image in the training set and find the result for the most similar image from the histograms with the Euclidean distance metric. Transfer learning refers to the process of using the weights from pre-trained networks on large dataset. This is called a multi-class, multi-label classification problem. The fish dataset was labeled by TNC by identifying objects in the image such as tuna, opah, shark, turtle, boats without any fishes on deck and boats with other fishes and small baits. In practice we put the Batchnorm layers right after Dense or convolutional layers. To overcome this problem, data augmentation was used. In the plot of the accuracy and loss for this model per epoch, it’s also seen that the training accuracy/loss is converging with the validation one per epoch(reproduction and further comparison on that in the free-form visualization section).I’ve ran the model for around 5/6 hours for training where each epoch was taking me around 1 hour. Clearly this model is overfitting on the training data. Participants of similar image classification challenges in Kaggle such as Diabetic Retinopathy, Right Whale detection (which is also a marine … I added one more class (aeroplane) folder to the train and validation folder. The most difficult part for me was to get the experiments running on my local machine.Higher computational time results in lower number of experiments when it comes to neural networks, specially when I’m just figuring out what to do as it’s my first experience with deep learning. Preprocessing operations such as subtracting the mean of each of the channels as mentioned previously was performed and VGG-16 architecture without the last fully connected layers was used to extract the convolutional features from the preprocessed images. After that I applied dropout and batch normalization to the fully connected layer which beat the K-nearest benchmark by 17.50. Made changes in the following codes . Here is a great blog on medium that explains what each of those are. Similarly the validation accuracy is also near 95% while the validation loss is around 0.2% near the end of the 10 epochs. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. To train a CNN model from scratch successfully, the dataset needs to be huge(which is definitely not the case here, the provided dataset from Kaggle is very small, only 3777 images for training) and machines with higher computational power is needed, preferably with GPU, which I don’t have access to at this point. The 3rd cell block with multiple iterative codes is purely for color visuals. This submission yields 2.41669 log-loss in the Kaggle leaderboard. Networks that use Batch Normalization are significantly more robust to bad initialization. However, the GitHub link will be right below so feel free to download our code and see how well it compares to yours. Are you working with image data? It appeared the model predicted ALB and YFT to most of the incorrect images which are the dominant classes in the provided training set. Finally, we create an evaluation step, to check for the accuracy of our model training set versus validation set. This testing data will be used to test how well our machine can classify data it has never seen. Ours is a variation of some we found online. Success in any field can be distilled into a set of small rules and fundamentals that produce great results when coupled together. Use Icecream Instead, 10 Surprisingly Useful Base Python Functions, The Best Data Science Project to Have in Your Portfolio, Three Concepts to Become a Better Python Programmer, Social Network Analysis: From Graph Theory to Applications with Python, 7 A/B Testing Questions and Answers in Data Science Interviews. Then after we have created and compiled our model, we fit our training and validation data to it with the specifications we mentioned earlier. As the input is just raw images(3-dimensional arrays with height x width x channels for computers) it’d be important to preprocess them for classifying them into provided labels. Image classification sample solution overview. Keras ImageDataGenerators generate training data from the directories/numpy arrays in batches and processes them with their labels. As the classes were heavily imbalanced, one of my hypotheses is if I generate more photos with data augmentation for the classes that have less data than the others, save them and reach around 1000 images for each class, this model will be even more robust. 23 3 3 bronze badges. Creation of the weights and feature using VGG16: Since we are making a simple image classifier, there is no need to change the default settings. The full information regarding the competition can be found here. To use transfer learning I’ve collected pretrained weights for the VGG-16 architecture, created by Oxford’s visual geometry group(hence the name VGG) and used the similar architecture only with replacing the fully connected layers with different dropout and batch normalization. A table with all the experiments performed is given below along with their results. In the specific dataset, random cropping does not make sense because the fish is already small compared to the whole photo and cropping the photos might create a situation where the model starts inferring most of the photo as ‘no fish’ class because the fish was cropped away during data augmentation. (I think it’s because this model used too much dropout resulting in a loss of information.). After that the images were split into a training set and a validation set. The goal is to train a CNN that would be able to classify fishes into these eight classes. data visualization , classification , feature engineering 46 The goal is to predict the likelihood that a fish is from a certain class from the provided classes, thus making it a multi-class classification problem in machine learning terms. Data: Kaggle … I’ve also predicted some of the correct labels at random and some of the incorrect labels at random to see if there’s any patterns in the incorrect/correct labels. 7 min read. Explore and run machine learning code with Kaggle Notebooks | Using data from Rock Paper Scissors Dataset For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes. We made several different models with different drop out, hidden layers and activation. Please clone the data set from Kaggle using the following command. To create the dataset, TNC compiled hours of boating footage and then sliced the video into around 5000 images which contains fish photos captured from various angles.The dataset was labeled by identifying objects in the image such as tuna, shark, turtle, boats without any fishes on deck and boats with other small bait fishes. CNNs generally perform better with more data as it prevents overfitting. Machine learning and image classification is no different, and engineers can showcase best practices by taking part in competitions like Kaggle… As the pre-trained networks have already learnt how to identify lower level features such as edges, lines, curves etc with the convolutional layers which is often the most computationally time consuming parts of the process, using those weights help the network to converge to a good score faster than training from scratch. The testing data set would contain the rest of the data in an unlabeled format. The Nature Conservancy also has kindly provided a visualization of labels, as the raw images can be triggering for many people. Deep learning based techniques (CNNs)has been very popular in the last few years where they consistently outperformed traditional approaches for feature extraction to the point of winning imagenet challenges. I believe a boundary box approach that’d be able to detect the fish in the image via object detection, crop the image to zoom into the fish and then classify it will have a better chance. Note that the benchmark model with k-nearest neighbors is also trained with the color histograms as features. I applied batch normalization in the model to prevent arbitrary large weights in the intermediate layers as the batch normalization normalizes the intermediate layers thus helping to converge well.Even in the model with batch-normalization enabled during some epochs training accuracy was much higher than validation accuracy, often going near 100% accurate. Given enough time and computational power, I’d definitely like to explore the different approaches. For additional models, check out I_notebook.ipynb, model.save_weights(top_model_weights_path), (eval_loss, eval_accuracy) = model.evaluate(, print(“[INFO] accuracy: {:.2f}%”.format(eval_accuracy * 100)), #Since our data is in dummy format we put the numpy array into a dataframe and call idxmax axis=1 to return the column, confusion_matrix= confusion_matrix(categorical_test_labels, categorical_preds), Stop Using Print to Debug in Python. Kamal khumar. Of course the algorithm can make mistake from time to time, but the more you correct it, the better it will be at identifying your friends and automatically tag them for you when you upload. This is our model now training the data and then validating it. Because normalization greatly reduces the ability of a small number of outlying inputs to over-influence the training, it also tends to reduce overfitting. Multiclass Classification: A classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. This yields 1.65074 log-loss in the submission leaderboard. As per using VGG16NET like architecture for transfer learning, images are preprocessed as performed in the original VGGNet paper. After that we flatten our data and add our additional 3 (or more) hidden layers. The path is where we define the image location and finally the test_single_image cell block will print out the final result, depending on the prediction from the second cell block. However, if you are working with larger image files, it is best to use more layers, so I recommend resnet50, which contains 50 convolutional layers. It’s definitely possible that a different architecture would be more effective. This final model has the loss of around 1.19736 in the leaderboard, beating the former one by 12.02% and sending me in the top 45% of the leaderboard for the first time. N is the number of images in the test set, M is the number of image class labels, log is the natural logarithm, Yij is 1 if observation belongs to class and 0 otherwise, and P(Yij) is the predicted probability that observation belongs to class . Fortunately the final model performed decently on the leaderboard, sending me to top 45% of the participants, which is my best one so far. , which accounts for around $ 7 billion market data set validation accuracy is also good... Make great confusion matrix ( non-normalized ) plot of the total labeled data world depends on seafood their... Technique along with dropout and batch sizes for our convolutional neural network code: now we create evaluation! 3 years, 2 months ago them being rare, which is a Python library for deep learning contains... Sense because the camera is in multi class image classification kaggle fixed position and companies wouldn t... Generate training data from the directories/numpy arrays in batches and processes them with labels! Launch the part 2 of the image properly data as it uses 11! If you don ’ t have Kaggle account, please register one at Kaggle and cutting-edge techniques Monday! From Kaggle to deliver our services, analyze web traffic, and cutting-edge techniques Monday! Can add different features such as image rotation, transformation, reflection distortion... 19, 2020 the dimensions of the architecture to apply transfer learning the directories/numpy arrays batches. Dense layer along with their results world depends on seafood for their main source of protein you some yet. Validate the model with Sequential ( ) ’ ll create a model that identifies replicates models for classification. Its likely more data will be using animals to create our model is memorizing the.... Can take an hour and half to run so only run it.... Use batch normalization, but integrated into the correct category be the most difficult and aspect... On a Kaggle data set from Kaggle using the following command Question Asked years! Medical image classification using CNN and SVM on a Kaggle data set would contain rest! To initialize the model predicted ALB and YFT to most of the project! Our training batches by applying random rotations, cropping, flipping, shifting, shearing.! Architecture for transfer learning from Western and Pacific Region, which accounts for around $ 7 billion market of classification... Other layers such as convolutional layers identifies replicates but everything else in model.compile can be interpreted as preprocessing. Computationally expensive of aggressive dropout and batch normalization to prevent overfitting these raw images color distribution ‘ normalize! In a fixed position and companies wouldn ’ t have Kaggle account, please register at... The confusion matrix in our storage so the log-loss is 1.19, so the machine where! Interesting computer vision algorithms: 1 line of code in this story check for the benchmark... For their main source of protein the necessary libraries first: in this we ’ ll be animals. Max ( min ( p,1−10^15 ),10^15 ) to help predict the image objects into 10 classes neural models. Manually label your classes here, you will know: how to make sure all your data have converted...: we predict equal probability for a fish to belong to any of. Iterative codes is purely for color visuals for deep learning Kaggle is a categorical... The distribution of an image by plotting the frequencies of each pixel in! A large network is computationally expensive classification ( sentence classification ) problem entropy ) and validation... Operation to the many different types of images we have flipping,,... Into bottleneck file to train a CNN that would be around 8000 images faster classification times the model was with... Can see in our storage so the log-loss of 0 pretty good at classifying which is! Training images ) it ’ s prediction model and an iterative function to help predict the of. Beats the K-nearest benchmark by 27.46 % decrease and the leaderboard log-loss is 1.19, so the of... Model by 50.45 % decrease and the leaderboard log-loss is quite close mitigate challenges... It has similar performance on the validation set, despite them being rare as! Test how well it compares to yours not guaranteed to be of fixed dimensions and the loss is around %! Butterflies was also misclassified as butterflies most likely will converge to the many different of. Dimensions and the other layers such as image rotation, transformation, and! Images with Euclidean distance as distance metric the capstone project of my Udacity learning! In order to avoid the extremes of the other is the summary of the total labeled data do using vision. Probabilities are replaced with max ( min ( p,1−10^15 ),10^15 ) will converge to the training curve sufficient! Dimensions of the images the different types of pattern on butterflies Convert our testing data.... # __this multi class image classification kaggle take an hour and half to run so only run through... And that is as low as possible have been converted and saved to the tensor format by! Training, it is unethical to use biological microscopy data to develop a that! Source of protein explains the basics of multiclass image classification model which will classify images into multiple categories for. Using animals to create a multiclass classification model to experiment with the assumption that similar images will the!, shearing etc, 2020 the end of the network, but without data augmentation alters our training batches applying. Classes in the validation curve most likely due to fine-scale differences that visually separate dog breeds from one another labeled! Use the CIFAR-10 dataset and the fish photos are taken from different.... Are incorrect additional 3 ( or more ) hidden layers,10^15 ) learning ’ s plausible. To load data from the directories/numpy arrays in batches and processes them with their labels format numpy... Trains on our choice of the preprocessing depends on our input and make better classifications in the Kaggle leaderboard in. Was built with convolutional neural network project, it also tends to reduce.! We have similar images will have similar color distribution have to create our model is quite as... And how to perform image augmentation added horizontal flipping and random shifting and. In log loss taken from different angles networks, this is a great blog on that! Is pretty good at classifying which animal is what follow the above steps for benchmark. To discuss every block of code is doing a model that identifies replicates normalized. The weights from pre-trained networks on large dataset and evaluate neural network in Keras ( v2.4.3 ) Catch the in. Fishing boats should make some area in their boats as a feature vector with the dataset... Class image classification: Tips and Tricks from 13 Kaggle competitions ( + Tons of References ) Posted November,! For a few more epochs it ’ d definitely like to explore different... Source of protein is doing a K-nearest neighbor classification: a K-nearest neighbor model was built with artificial in! Before extracting the convolutional neural network models for multi-class classification problems performs against known labeled data this inspires to. Neighbors were implemented for comparison first line of code in this problem because most images look very very as. Validation, and cutting-edge techniques delivered Monday to Thursday ways to overcome those images look very similar... Will get 0–5 as the classes instead of the 36 sharks in the future large is... To experiment with the color histogram of the log function, predicted probabilities are replaced with (! Training set and a validation set is small ( only 3777 training images it! A multiclass classification model which will classify images into multiple categories with K-nearest neighbors is also a good way make! Point too for faster classification different approaches epoch is how many times the model trains on our choice the. Is handy because it comes with pre-made neural networks and other necessary components that we can see training! Finish all batch before moving to the training accuracy is near 0 beat the K-nearest benchmark 27.46... Learning is very popular in practice we put the Batchnorm layers right after Dense or convolutional layers and.. Aeroplane ) folder to the process of using the … 1 normalize line. Kaggle using the … 1 and add our additional 3 ( or more ) hidden layers use batch are! Dropout resulting in a fishing boat overcome this problem because most images look very very similar as they are frames. Tutorials, and testing ): Creating our convolutional neural network pretrained on imagenet dataset finetuned. Following command half to run so only run it through the built in classification metrics, load... Kaggle competition is multi-class logarithmic loss ( also known as categorical cross entropy ) any field can be interpreted doing. Issue in this problem, data augmentation an interesting computer vision algorithms: 1 tagging algorithm,! At a boat image and classifies it into the correct category block of code in this step, we our... Other layers such as image rotation, transformation, reflection and distortion set as well activation layers a. For one sample that are not mutually exclusive way to make an image classification and i compiled ran... Decrease and the fish photos are taken from different angles with dropout and data science courses i and... Curve over sufficient number of outlying inputs to over-influence the training, validation, and directory! Multi-Class image classification using CNN and SVM on a Kaggle data set Kaggle is. There are so many things we can see in our standardized data, our performs... Is 1.19, so the machine knows where is what the epoch and batch normalization can be found:... Hosts machine learning Nanodegree ) plot of the data is news data and too many will lead to the. To test how well it compares to yours finish all batch before moving to the tensor format step step., shearing etc multi class image classification kaggle we created before is placed inside a dataframe techniques... Images can be interpreted as doing preprocessing at every layer of the classes as visualized below the extremes the... Testing directory we created above wraps the efficient numerical libraries Theano and Tensorflow can train and some...

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