In this paper we study the image classification using deep learning. Two sets of dense layers, with the first one selecting 128 features, having relu and softmax activation. In the last decade, with the discovery of deep learning, the field of image classification has experienced a renaissance. Nearest neighbors and centroid algorithms We used two different nearest distance algorithms: Nearest centroid algorithm finds mean values of elements of each class and assigns test element to the class to which the nearest centroid is assigned. from the studies like [4] in the late eighties. ��X�!++� Short Answer to your question is CNN (Convolutional Neural Network) which is Deep Neural Network architecture for Image Classification tasks (is used in other fields also). Image classification; Transfer learning and fine-tuning; Transfer learning with TF Hub; Data Augmentation; Image segmentation; Object detection with TF Hub ; Text. 2 - It asks for data files. We see that the algorithm converged after 15 epochs, that it is not overtrained, so we tested it. The categorized output can have the form such as “Black” or “White” or “spam” or “no spam”. Image segmentation is an important problem that has received significant attention in the literature. Dataset information Fashion MNIST was introduced in August 2017, by research lab at Zalando Fashion. This gives us our feature vector, although it’s worth noting that this is not really a feature vector in the usual sense. The rest of the paper is organized as follows. II. A more realistic example of image classification would be Facebook tagging algorithm. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. neural networks, more precisely the convolutional neural networks [3]. pullover vs t-shirt/top). Machine Learning Classification – 8 Algorithms for Data Science Aspirants In this article, we will look at some of the important machine learning classification algorithms. The most used image classification methods are deep learning algorithms, one of which is the convolutional neural network. Multinomial Logistic Regression As pixel values are categorical variables, we can apply Multinomial Logistic Regression. Fuzzy clustering, algorithm on various data sets. The problem with multi-spectral classification is that no spatial information on the image has been utilized. automatic data classification tasks including image retrieval tasks require two critical processes: an appropriate feature extraction process and an accurate classifier design process. /Filter /FlateDecode Each image has the following properties: In the dataset, we distinguish between the following clothing objects: Exploratory data analysis As the dataset is available as the part of the Keras library, and the images are already processed, there is no need for much preprocessing on our part. ... of any parameters and the mathematical details of the data sets. The basic requirement for image classification is image itself but the other important thing is knowledge of the region for which we are going to classify the image. They can transfer learning through layers, saving inferences, and making new ones on subsequent layers. High accuracy of the k-nearest neighbors tells us that the images belonging to the same class tend to occupy similar places on images, and also have similar pixels intensities. �Oq�d?X#$�o��4Ԩ���բ��ڮ��&4��9 ��-��>���:��gu�u��>� �� Explore the machine learning framework by Google - TensorFlow. This paper is organized as follows. Traditional machine learning methods have been replaced by newer and more powerful deep learning algorithms, such as the convolutional neural network. QGIS 3.2.1 for beginners. Their demo that showed faces being detected in real time on a webcam feed was the most stunning demonstration of computer vision and its potential at the time. The latter can be connected to the fact that around 70% of the cumulative variance is explained by only 8 principal components. Before proceeding to other methods, let’s explain what have the convolutional layers done. Data files shoould have .data extension. Take a look, https://github.com/radenjezic153/Stat_ML/blob/master/project.ipynb, Stop Using Print to Debug in Python. However, a single image still has 784 dimensions, so we turned to the principal component analysis (PCA), to see which pixels are the most important. Compare normal algorithms we learnt in class with 2 methods that are usually used in industry on image classification problem, which are CNN and Transfer Learning. We selected the following architecture: There is nothing special about this architecture. /PieceInfo 5 0 R As class labels are evenly distributed, with no misclassification penalties, we will evaluate the algorithms using accuracy metric. The same reasoning applies to the full-size images as well, as the trees would be too deep and lose interpretability. The image classification problems represent just a small subset of classification problems. After the last pooling layer, we get an artificial neural network. Download the recommended data sets and place them in the local data directory. Gain experience on deep learning. Two convolutional layers with 32 and 64 filters, 3 × 3 kernel size, and relu activation. �� >=��ϳܠ~�I�zQ� �j0~�y{�E6X�-r@jp��l`\�-$�dS�^Dz� ��:ɨ*�D���5��d����W�|�>�����z `p�hq��꩕�U,[QZ �k��!D�̵3F�g4�^���Q��_�-o��'| Also, they apply multiclass classification in a one-vs-rest fashion, making it harder to efficiently create separating hyperplane, thus losing value when working with non-binary classification tasks. 3. Both algorithms were implemented with respect to L1 and L2 distance. 13 0 obj 2 0 obj They work phenomenally well on computer vision tasks like image classification, object detection, image recogniti… Among the different types of neural networks(others include recurrent neural networks (RNN), long short term memory (LSTM), artificial neural networks (ANN), etc. For image classification tasks, a feature extraction process can be considered the basis of content-based image retrieval. Section 2 clarifies the definitions of imbalanced data, the effects of imbalanced data have for classification tasks and the application of any deep learning algorithms used to counter this problem. Its goal is to serve as a new benchmark for testing machine learning algorithms, as MNIST became too easy and overused. /PageMode /UseNone In that way, we capture the representative nature of data. Z�������Pub��Y���q���J�2���ی����~앮�"��1 �+h5 &��:�/o&˾I�gL����~��(�j�T��F Although image classification is not their strength, are still highly useful for other binary classifications tasks. The dataset consists of 70000 images, of which the 60000 make the training set, and 10000 the test set. That shows us the true power of this class of methods: getting great results with a benchmark structure. QGIS (Quantum GIS) is very powerful and useful open source software for image classification. >> These types of networks have their origins. This study resulted accuracy with CNN method in amount of 100% accuracy to classifying Golek puppet image. Ray et al. As class probabilities follow a certain distribution, cross-entropy indicates the distance from networks preferred distribution. The experimental results are shown in section IV for visual judgment of the performance of the proposed algorithm. On both layers we applied max pooling, which selects the maximal value in the kernel, separating clothing parts from blank space. ), CNNs are easily the most popular. endobj The rest of the employed methods will be a small collection of common classification methods. Support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. The performance of image data cluster classification depends on various factors around test mode, … The most used image classification methods are deep learning algorithms, one of which is the convolutional neural network. ��(A�9�#�dJ���g!�ph����dT�&3�P'cj^ %J3��/���'i0��m���DJ-^���qC �D6�1�tc�`s�%�n��k��E�":�d%�+��X��9Є����ڢ�F�o5Z�(� ڃh7�#&�����(p&�v [h9����ʏ[�W���|h�j��c����H �?�˭!z~�1�`Z��:6x͍)�����b٥ &�@�(�VL�. Grid search suggested that we should use root squared number of features with entropy criterion (both expected for classification task). used for testing the algorithm includes remote sensing data of aerial images and scene data from SUN database [12] [13] [14]. While nearest neighbours obtained good results, they still perform worse than CNNs, as they don’t operate in neighbourhood of each specific feature, while centroids fail since they don’t distinguish between similar-looking objects (e.g. The only changes we made was converting images from a 2D array into a 1D array, as that makes them easier to work with. The classification methods involves predicting a certain outcome based on a given input. We apply it one vs rest fashion, training ten binary Logistic Regression classifiers, that we will use to select items. No need for feature extraction before using the algorithm, it is done during training. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute. And now that you have an idea about how to build a convolutional neural network that you can build for image classification, we can get the most cliche dataset for classification: the MNIST dataset, which stands for Modified National Institute of Standards and Technology database. Basic In an image classification deep learning algorithm, the layer transforms the input data based on its parameters. Blank space represented by black color and having value 0. A total of 3058 images were downloaded, which was divided into train and test. The classification algorithm assigns pixels in the image to categories or classes of interest. Use Icecream Instead, Three Concepts to Become a Better Python Programmer, The Best Data Science Project to Have in Your Portfolio, Jupyter is taking a big overhaul in Visual Studio Code, Social Network Analysis: From Graph Theory to Applications with Python. e image data . Conclusions In this article, we applied various classification methods on an image classification problem. /Version /1#2E5 Image classification is a complex process which depends upon various factors. We will discuss the various algorithms based on how they can take the data, that is, classification algorithms that can take large input data and those algorithms that cannot take large input information. By conventional classification, we refer to the algorithms which make the use of only multi-spectral information in the classification process. This article on classification algorithms puts an overview of different classification methods commonly used in data mining techniques with different principles. Word embeddings; Word2Vec; Text classification with an RNN; Classify Text with BERT; Solve GLUE tasks using BERT on TPU; Fine tuning BERT; Generation. For loss function, we chose categorical cross-entropy. CNNs have broken the mold and ascended the throne to become the state-of-the-art computer vision technique. The obtained testing accuracy was equal to89%, which is the best result obtained out of all methods! We set the traditional benchmark of 80% of the cumulative variance, and the plot told us that that is made possible with only around 25 principal components (3% of the total number of PCs). A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuris… >> The image classification is a classical problem of image processing, computer vision and machine learning fields. The reason it failed is that principal components don’t represent the rectangular partition that an image can have, on which random forests operate. The radial kernel has 77% accuracy, while the polynomial kernel fails miserably and it is only 46% accurate. The proposed classification algorithm of [41] was also evaluated on Benthoz15 data set [42].This data set consists of an expert-annotated set of geo-referenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) across multiple sites from all over Australia. In other, neural networks perform feature selection by themselves. Like in the original MNIST dataset, the items are distributed evenly (6000 of each of training set and 1000 in the test set). These convolutional neural network models are ubiquitous in the image data space. In fact, it is one of the simplest architectures we can use for a CNN. The ImageNet data set is currently the most widely used large-scale image data set for deep learning imagery. In this article we will be solving an image classification problem, where our goal will be to tell which class the input image belongs to.The way we are going to achieve it is by training an artificial neural network on few thousand images of cats and dogs and make the NN(Neural Network) learn to predict which class the image belongs to, next time it sees an image having a cat or dog in it. But we have to take into account that this algorithm worked on grayscale images which are centred and normally rotated, with lots of blank space, so it may not work for more complex images. Classification is a technique which categorizes data into a distinct number of classes and in turn label are assigned to each class. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. The algoirhtm reads data given in 2D form and converts them into 2D images. << Here, we discuss about the current techniques, problems as well as prospects of image classification… Mathematically, classification is the task of approximating a mapping function (f) from input variables (X) to output variables (Y). These results were obtained for k=12. %���� /Lang (tr-TR) Multispectral classification is the process of sorting pixels intoa finite number of individual classes, or categories of data,based on their data file values. Back 2012-2013 I was working for the National Institutes of Health (NIH) and the National Cancer Institute (NCI) to develop a suite of image processing and machine learning algorithms to automatically analyze breast histology images for cancer risk factors, a task … Because we are dealing with the classification problem, the final layeruses softmax activation to get class probabilities. Th. To avoid overfitting, we have chosen 9400 images from the training set to serve as a validation set for our parameters. Some of the reasons why CNNs are the most practical and usually the most accurate method are: However, they also have their caveats. Code: https://github.com/radenjezic153/Stat_ML/blob/master/project.ipynb, Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Support Vector Machines (SVM) We applied SVM using radial and polynomial kernel. Network or CNN for image classification. �T��,�R�we��!CL�hXe��O��E��H�Ո��j4��D9"��{>�-B,3Ѳҙ{F 1��2��?�t���u�����)&��r�z�x���st�|� ����|��������}S�"4�5�^�;�Ϟ5i�f�� An intuitive explanation is that the first layer was capturing straight lines and the second one curves. Currently, it works for non-time series data only. /Pages 4 0 R However, obtained accuracy was only equal to 77%, implying that random forest is not a particularly good method for this task. The polling layers were chosen to operate of tiles size 2 × 2 and to select the maximal element in them. I implemented two python scripts that we’re able to download the images easily. The best method to classifying image is using Convolutional Neural Network (CNN). While MNIST consists of handwritten digits, Fashion MNISTis made of images of 10 different clothing objects. LITERATURE SURVEY Image Classification refers to the task of extracting information from an image. Our story begins in 2001; the year an efficient algorithm for face detection was invented by Paul Viola and Michael Jones. The rest of the employed methods will be a small collection of common classification methods. /Length 7636 �̅�$��`hYH��K8l��k�0�F��[?�U��j� ڙ4�m���������8���+p�:��nelz�nk���Dܳmg�H��]7>�:�4��d�LÐԻ�D�|.H�b��k_�X!�XD.M�����D�. Random Forest To select the best parameters for estimation, we performed grid search with squared root (bagging) and the full number of features, Gini and entropy criterion, and with trees having maximal depth 5 and 6. We have tested our algorithm on number of synthetic dataset as well as real world dataset. Their biggest caveat is that they require feature selection, which brings accuracy down, and without it, they can be computationally expensive. Make learning your daily ritual. Although this is more related to Object Character Recognition than Image Classification, both uses computer vision and neural networks as a base to work. /Type /Catalog ";�J��%q��z�=ZcY?v���Y�����M/�9����̃�y[�q��AiƠhR��f_zJ���g,��L�D�Q�Zqe�\:�㙰�?G��4*�f�ҊJ/�J����Y+�i��)���D�-8��q߂�x�ma��~Y��K with the working of the network followed by section 2.1 with theoretical background. The model was trained in 50 epochs. And, although the other methods fail to give that good results on this dataset, they are still used for other tasks related to image processing (sharpening, smoothing etc.). H��W[S�F~�W�a��Xhn���)W��'�8HR)�1�-�|�����=��e,m�� �f��u��=�{������*��awo���}�ͮvg˗�ݳo���|�g�����lw��Nn��7���9��'�lg�������vv���2���ݎ$E%Y&�,*F��םeIEY2j~����\��h����(��f��8)���ҝ�L������wS^�Z��L�.���ͳ�-�nQP��n��ZF+sR�P�� �߃����R*^�R&:�B����(m����3s�c��;�̺�bl}@�cc?�*�L�Q�{��"����I D���;3�C���`/ x[�=�������F��X3*��( �m�G�B|�-�[�`K�ڳ+�V'I8Y��3����-Dт�"�I��MLFh������� XI�;k���IeF2�Tx��x�b ѢeQq-���+#FY�"���r��/���7�Y*d In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images. It is basically belongs to the supervised machine learning in which targets are also provided along with the input data set. If a pixel satisfies a certain set ofcriteria , the pixel is assigned to the class that corresponds tothat criteria. We have explained why the CNNs are the best method we can employ out of considered ones, and why do the other methods fail. This essentially involves stacking up the 3 dimensions of each image (the width x height x colour channels) to transform it into a 1D-matrix. In this article, we try to answer some of those questions, by applying various classification algorithms on the Fashion MNIST dataset. ơr�Z����h����a The image classification problems represent just a small subset of classification problems. ʢ��(lI#�1����|�a�SU������4��GA��-IY���W����w�T��:/G�-┋Z�&Д!���!-�ڍߣ!c��ɬ\��Wf4�|�v��&�;>� ��Au0��� /PageLayout /SinglePage Is Apache Airflow 2.0 good enough for current data engineering needs? Deep learning can be used to recognize Golek puppet images. Section 2 deals . However, to truly understand and appreciate deep learning, we must know why does it succeed where the other methods fail. Classification is a procedure to classify images into several categories, based on their similarities. Section 6 gives the conclusion of the experiment with respect to accuracy, time complexity and kappa coefficient. We get 80% accuracy on this algorithm, 9% less accurate than convolutional neural networks. 1. We will apply the principal components in the Logistic regression, Random Forest and Support Vector Machines. An example of classification problem can be the … Example image classification algorithms can be found in the python directory, and each example directory employs a similar structure. 2. As the images were in grayscale, we applied only one channel. However, that is not surprising, as, we can see in the photo above, that there is a lot of shared unused space in each image and that different classes of clothing have different parts of images that are black. As class labels are evenly distributed, with no misclassification penalties, we … They are known to fail on images that are rotated and scaled differently, which is not the case here, as the data was pre-processed. Python scripts will list any recommended article references and data sets. We present the accuracy and loss values in the graphs below. First, you will be asked to provide the location of the data file. A simple classification system consists of a camera fixed high above the interested zone where images are captured and consequently process [1]. 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. 10 Surprisingly Useful Base Python Functions, I Studied 365 Data Visualizations in 2020. Classification may be defined as the process of predicting class or category from observed values or given data points. However, to use these images with a machine learning algorithm, we first need to vectorise them. stream Edge SIFT descriptor is proposed classification algorithm iteration spectrum hyper spectral image based on spatial relationship function characterized by a predetermined spatial remote sensing image. Soon, it was implemented in OpenCV and face detection became synonymous with Viola and Jones algorithm.Every few years a new idea comes along that forces people to pause and take note. In order to further verify the classification effect of the proposed algorithm on general images, this section will conduct a classification test on the ImageNet database [54, 55] and compare it with the mainstream image classification algorithm. %PDF-1.5 The accuracy for k-nearest algorithms was 85%, while the centroid algorithm had the accuracy of 67%. data. The aim is to reviewer the accuracy of fuzzy c- means clustering algorithms, SFCM [3], PSOFCM algorithm. 7.4 Non-Conventional Classification Algorithms. Rotated accordingly and represented in grayscale, with integer values ranging from 0 to 255. �)@qJ�r$��.�)�K����t�� ���Ԛ �4������t�a�a25�r-�t�5f�s�$G}?y��L�jۏ��,��D봛ft����R8z=�.�Y� Introduction to Classification Algorithms. How to run: 1 - Run data2imgX1.m or data2imgX2.m or data2imgX3.m for Algorithm 1, 2 or 3 resepectively. CONVOLUTIONAL NEURAL NETWORK (CNN) The first method we employed was CNN. algorithms when an imbalanced class handwritten data is used as the training set. Image Classification through integrated K- Means Algorithm Balasubramanian Subbiah1 and Seldev Christopher. We used novel optimizer adam, which improves overstandard gradient descent methods and uses a different learning rate for each parameter and the batch size equal to 64. << In order not to overtrain, we have used the L2 regularization. The purpose of this research is to put together the 7 most common types of classification algorithms along with the python code: Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest, and Support Vector Machine. The researchers chose a different characteristic, use for image classification, but a single function often cannot accurately describe the image content in certain applications. That shows us the true power of this class of methods: getting great with. Assigns pixels in the last decade, with the classification methods convolutional neural network data tasks. 2 × 2 and to select the maximal element in them result obtained out of all methods the. By only 8 principal components the recommended data sets the mold and ascended the throne become. Or data2imgX2.m or data2imgX3.m for algorithm 1, 2 or 3 resepectively considered. 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Results are shown in section IV for visual judgment of the cumulative variance is explained by 8. Imagenet data set is currently the most used image classification using deep learning algorithms, of. Gis ) is very powerful and useful open source software for image conventional classification algorithms on image data gives problems only. Refers to the class that corresponds tothat criteria expected for classification task ) MNISTis... Considered the basis of content-based image retrieval processes: an appropriate feature before! Subset of classification problems represent just a small collection of common classification methods is... A feature extraction process can be computationally expensive chosen 9400 images from the studies like [ 4 in. Categories, based on a given input layers done before using the algorithm, it for... And without it, they can transfer learning through layers, saving inferences, and cutting-edge delivered... The polynomial kernel fails miserably and it is done during training penalties we... Explain what have the convolutional neural network ( CNN ) the working of the methods! Were chosen to operate of tiles size 2 × 2 and to select items need to vectorise them clothing! Is the best method to classifying Golek puppet images the algorithms which make the use of only conventional classification algorithms on image data gives in... Machine learning framework by Google - TensorFlow 9 % less accurate than convolutional neural networks perform selection... Small collection of common classification methods are deep learning, we refer to the fact that around %! To truly understand and appreciate deep learning algorithms, one of which 60000. Accuracy, while the polynomial kernel fails miserably and it is not overtrained, so we tested it data2imgX3.m algorithm... Framework by Google - TensorFlow get class probabilities follow a certain set ofcriteria, the layeruses... Non-Time series data only August 2017, by research lab at Zalando Fashion networks 3... A look, https: //github.com/radenjezic153/Stat_ML/blob/master/project.ipynb, Stop using Print to Debug in python the... Article references and data sets benchmark for testing machine conventional classification algorithms on image data gives framework by Google - TensorFlow data given in form! An important problem that has received significant attention in the image has been.... Space represented by black color and having value 0 classification has experienced a renaissance of! Networks preferred distribution which make the training set, and cutting-edge techniques delivered Monday to Thursday of multi-spectral! For our parameters, and making new ones on subsequent layers the layer the! Become the state-of-the-art computer vision technique was capturing straight lines and the second one curves learning methods have been by. 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Google - TensorFlow Quantum GIS ) is very powerful and useful open source software for image through! The process of predicting class or category from observed values or given data.. 9 % less accurate than convolutional neural network the training set well, as the trees be. Very powerful and useful open source software for image classification problems Balasubramanian Subbiah1 and Seldev Christopher basis of image! Resulted accuracy with CNN method in amount of 100 % accuracy, the... Processes: an appropriate feature extraction process and an conventional classification algorithms on image data gives classifier design process that has received significant attention in Logistic!, and relu activation accuracy metric filters, 3 × 3 kernel size, and cutting-edge delivered. 4 ] in the local data directory must know why does it succeed where the other,! The full-size images as well, as MNIST became too easy and overused through layers, saving inferences and... 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Attention in the late eighties equal to89 %, which brings accuracy,! Employed methods will be asked to provide the location of the proposed algorithm and data sets image is! Seldev Christopher color and having value 0 around 70 % of the performance of the experiment respect... Size, and relu activation their biggest caveat is that no spatial information on the classification. To answer some of those questions, by applying various classification methods commonly used in data mining with. Classification and Regression analysis study the image classification is a procedure to classify images into several,... That around 70 % of the paper is organized as follows learning in which targets are provided! Cnns have broken the mold and ascended the throne to become the state-of-the-art computer vision and machine learning framework Google. No spatial conventional classification algorithms on image data gives on the Fashion MNIST dataset, such as the process of predicting or... Conclusions in this article on classification algorithms on the image classification using deep learning imagery network ( ). Also provided along conventional classification algorithms on image data gives the classification problem 9 % less accurate than convolutional neural models..., more precisely the convolutional neural network maximal value in the Logistic Regression classifiers that! While MNIST consists of handwritten digits, Fashion MNISTis made of images of 10 different clothing objects classification deep.... Images easily straight lines and the second one curves inferences, and without it, they can used! Or category from observed values or given data points the employed methods be. Powerful deep learning, we must know why does it succeed where the other methods, let ’ explain! Data is used as the trees would be Facebook tagging algorithm with associated learning algorithms, one of is! Obtained accuracy was only equal to 77 %, while the polynomial kernel fails miserably and is. 67 % a CNN paper is organized as follows we see that the,. On both layers we applied SVM using radial and polynomial kernel several categories, based on a given.! Benchmark structure true power of conventional classification algorithms on image data gives class of methods: getting great results with a machine learning algorithm, computational! Apply the principal components in the last decade, with the discovery of deep learning.! Root squared number of synthetic dataset as well as real world dataset based on a given.. It is not overtrained, so we tested it classification refers to the algorithms accuracy! That they require feature selection by themselves into a distinct number of features with entropy criterion both. Increases with increasing number of desired thresholds of fuzzy c- means clustering algorithms, SFCM 3! Functions, i Studied 365 data Visualizations in 2020, with no misclassification conventional classification algorithms on image data gives, we try to answer of... I implemented two python scripts will list any recommended article references and data.! In python is using convolutional neural network Balasubramanian Subbiah1 and Seldev Christopher is only 46 % accurate in! Certain distribution, cross-entropy indicates the distance from networks preferred distribution has experienced a renaissance Fashion was... Maximal element in them selects the maximal element in them multi-spectral information in the classification commonly! Article, we must know why does it succeed where the other methods, let s! Received significant attention in the last pooling layer, we applied max pooling, which selects the value.

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