max pooling vs average pooling

Robotic Companies 2.0: Horizontal Modularity, Most Popular Convolutional Neural Networks Architectures, Convolution Neural Networks — A Beginner’s Guide [Implementing a MNIST Hand-written Digit…, AlexNet: The Architecture that Challenged CNNs, From Neuron to Convolutional Neural Network, Machine Learning Model as a Serverless App using Google App Engine. Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated presence of a feature respectively. There is one more kind of pooling called average pooling where you take the average value instead of the max value. The output of the pooling method varies with the varying value of the filter size. The main purpose of a pooling layer is to reduce the number of parameters of the input tensor and thus - Helps reduce overfitting - Extract representative features from the input tensor - Reduces computation and thus aids efficiency. Average Pooling Layer. This gives us specific data rather than generalised data, deepening the problem of overfitting and doesn't deliver good results for data outside the training set. As you may observe above, the max pooling layer gives more sharp image, focused on the maximum values, which for understanding purposes may be the intensity of light here whereas average pooling gives a more smooth image retaining the … Pooling with the maximum, as the name suggests, it retains the most prominent features of the feature map. The operations are illustrated through the following figures. Max pooling operation for 3D data (spatial or spatio-temporal). But if they are too, it wouldn't make much difference because it just picks the largest value. In the last few years, experts have turned to global average pooling (GAP) layers to minimize overfitting by reducing the total number of parameters in the model. You may observe the greatest values from 2x2 blocks retained. However, the darkflow model doesn't seem to decrease the output by 1. These examples are extracted from open source projects. What would you like to do? Inputs are multichanneled images. Here is the model structure when I load the example model tiny-yolo-voc.cfg. We shall learn which of the two will work the best for you! As you may observe above, the max pooling layer gives more sharp image, focused on the maximum values, which for understanding purposes may be the intensity of light here whereas average pooling gives a more smooth image retaining the essence of the features in the image. This can be done by a logistic regression (1 neuron): the weights end up being a template of the difference A - B. The diagram below shows how it is commonly used in a convolutional neural network: As can be observed, the final layers c… You may check out the related API usage on the sidebar. UPDATE: The subregions for Sum pooling / Mean pooling are set exactly the same as for Max pooling but instead of using max function you use sum / mean. Keras documentation. This is maximum pooling, only the largest value is kept. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Strides values. In this short lecture, I discuss what Global average pooling(GAP) operation does. It is useful when the background of the image is dark and we are interested in only the lighter pixels of the image. … This is average pooling, average values are calculated and kept. Min Pool Size: 0: The minimum number of connections maintained in the pool. Max Pooling; Average Pooling; Max Pooling. Features from such images are extracted by means of convolutional layers. Max pooling is a sample-based discretization process. Max pooling step — final. I tried it out myself and there is a very noticeable difference in using one or the other. In this article, we have explored the significance or the importance of each layer in a Machine Learning model. August 2019. Region proposal: Given an input image find all possible places where objects can be located. Average Pooling - The Average presence of features is reflected. There is a very good article by JT Springenberg, where they replace all the max-pooling operations in a network with strided-convolutions. The author argues that spatial invariance isn't wanted because it's important where words are placed in a sentence. - global_ave.py. pytorch nn.moudle global average pooling and max+average pooling. Arguments. It keeps the average value of the values that appear within the filter, as images are ultimately a set of well arranged numeric data. And I guess compared to max pooling, strides would work just as well and be cheaper (faster convolution layers), but a variant I see mentioned sometimes is that people sum both average pooling and max pooling, which doesn't seem easily covered by striding. The down side is that it also increases the number of trainable parameters, but this is not a real problem in our days. However, if the max-pooling is size=2,stride=1 then it would simply decrease the width and height of the output by 1 only. Here is the model structure when I load the example model tiny-yolo-voc.cfg. In this tutorial, you will discover how the pooling operation works and how to implement it in convolutional neural networks. Min pooling: The minimum pixel value of the batch is selected. Skip to content. Pooling is performed in neural networks to reduce variance and computation complexity. The idea is simple, Max/Average pooling operation in convolution neural networks are used to reduce the dimensionality of the input. Mit Abstand am stärksten verbreitet ist das Max-Pooling, wobei aus jedem 2 × 2 Quadrat aus Neuronen des Convolutional Layers nur die Aktivität des aktivsten (daher "Max") Neurons für die weiteren Berechnungsschritte beibehalten wird; die Aktivität der übrigen Neuronen wird verworfen (siehe Bild). Its purpose is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. I normally work with text and not images. This is done by means of pooling layers. Source: Stanford’s CS231 course (GitHub) Dropout: Nodes (weights, biases) are dropped out at random with probability . You may observe by above two cases, same kind of image, by exchanging foreground and background brings a drastic impact on the effectiveness of the output of the max pooling layer, whereas the average pooling maintains its smooth and average character. Max Pooling Layer. Global max pooling = ordinary max pooling layer with pool size equals to the size of the input (minus filter size + 1, to be precise). This means that each 2×2 square of the feature map is down sampled to the average value in the square. And while more sophisticated pooling operation was introduced like Max-Avg (Mix) Pooling operation, I was wondering if we can do the … my opinion is that max&mean pooling is nothing to do with the type of features, but with translation invariance. Marco Cerliani. Here, we need to select a pooling layer. `"valid"` means no padding. Consider for instance images of size 96x96 pixels, and suppose we have learned 400 features over 8x8 inputs. All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. Maxpooling vs minpooling vs average pooling. Hence, filter must be configured to be most suited to your requirements, and input image to get the best results. To be honest, I don't remember super well (it was about a year ago). (2, 2, 2) will halve the size of the 3D input in each dimension. But average pooling and various other techniques can also be used. For nonoverlapping regions (Pool Size and Stride are equal), if the input to the pooling layer is n-by-n, and the pooling region size is h-by-h, then the pooling layer down-samples the regions by h. That is, the output of a max or average pooling layer for one channel of a convolutional layer is n / h -by- n / h . Above is variations in the filter used in the above coding example of average pooling. Similar variations maybe observed for max pooling as well. The last fully-connected layer is called the “output layer” and in classification settings it represents the class scores. Only the reduced network is trained on the data at that stage. But they present a problem, they're sensitive to location of features in the input. Args: pool_size: Tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). Just like a convolutional layer, pooling layers are parameterized by a window (patch) size and stride size. Maximum pooling is a pooling operation that calculates the maximum, or largest, value in each patch of each feature map. Arguments. Article from medium.com. Pooling layer is an important building block of a Convolutional Neural Network. Imagine learning to recognise an 'A' vs 'B' (no variation in A's and in B's pixels). It was a deliberate choice - I think with the examples I tried, max pooling looked nicer. Max pooling extracts only the most salient features of the data. For example, to detect multiple cars and pedestrians in a single image. The following image shows how pooling is done over 4 non-overlapping regions of the image. That is, the output of a max or average pooling layer for one channel of a convolutional layer is n/h-by-n/h. [61] Due to the aggressive reduction in the size of the representation, [ which? ] def cnn_model_max_and_aver_pool(self, kernel_sizes_cnn: List[int], filters_cnn: int, dense_size: int, coef_reg_cnn: float = 0., coef_reg_den: float = 0., dropout_rate: float = 0., input_projection_size: Optional[int] = None, **kwargs) -> Model: """ Build un-compiled model of shallow-and-wide CNN where average pooling after convolutions is replaced with concatenation of average and max poolings. Average Pooling Layer. Star 0 Fork 0; Star Code Revisions 1. There are quite a few methods for this task, but we’re not going to talk about them in this post. First in a fixed position in the image. For example, we may slide a window of size 2×2 over a 10×10 feature matrix using stride size 2, selecting the max across all 4 values within each window, resulting in a new 5×5 feature matrix. Keras documentation. Created Feb 23, 2018. When classifying the MNIST digits dataset using CNN, max pooling is used because the background in these images is made black to reduce the computation cost. For example, if the input of the max pooling layer is $0,1,2,2,5,1,2$, global max pooling outputs $5$, whereas ordinary max pooling layer with pool size equals to 3 outputs $2,2,5,5,5$ (assuming stride=1). In this article we deal with Max Pooling layer and Average Pooling layer. Many a times, beginners blindly use a pooling method without knowing the reason for using it. As we saw in the previous chapter, Neural Networks receive an input (a single vector), and transform it through a series of hidden layers. Different layers include convolution, pooling, normalization and much more. Average pooling makes the images look much smoother and more like the original content image. """Max pooling operation for 3D data (spatial or spatio-temporal). Sum pooling (which is proportional to Mean pooling) measures the mean value of existence of a pattern in a given region. pytorch nn.moudle global average pooling and max+average pooling. This fairly simple operation reduces the data significantly and prepares the model for the final classification layer. Region of interest pooling (also known as RoI pooling) is an operation widely used in object detection tasks using convolutional neural networks. ... Average pooling operation for 3D data (spatial or spatio-temporal). In short, in AvgPool, the average presence of features is highlighted while in MaxPool, specific features are highlighted irrespective of location. Max pooling uses the maximum value of each cluster of neurons at the prior layer, while average pooling instead uses the average value. Max Pooling Layer. Average pooling method smooths out the image and hence the sharp features may not be identified when this pooling method is used. Global Pooling Layers However, if the max-pooling is size=2,stride=1 then it would simply decrease the width and height of the output by 1 only. For example: the significance of MaxPool is that it decreases sensitivity to the location of features. Each convolution results in an output of size (96−8+1)∗(96−8+1)=7921, and since we have 400 features, this results in a vector of 892∗400=3,168,40… `(2, 2, 2)` will halve the size of the 3D input in each dimension. Max pooling is simply a rule to take the maximum of a region and it helps to proceed with the most important features from the image. The three types of pooling operations are: The batch here means a group of pixels of size equal to the filter size which is decided based on the size of the image. Pooling 'true' When true, the connection is drawn from the appropriate pool, or if necessary, created and added to the appropriate pool. The output of this stage should be a list of bounding boxes of likely positions of objects. Following figures illustrate the effects of pooling on two images with different content. It is the same as a traditional multi-layer perceptron neural network (MLP). Therefore, Max pooling, which is a form of down-sampling is used to identify the most important features. Priyanshi Sharma has been a Software Developer, Intern and a Computer Science student at National Institute of Technology, Raipur. Di Caro, D. Ciresan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. For overlapping regions, the output of a pooling layer is (Input Size – Pool Size + 2*Padding)/Stride + 1. Visit our discussion forum to ask any question and join our community, Learn more about the purpose of each operation of a Machine Learning model. To know which pooling layer works the best, you must know how does pooling help. Convolutional layers represent the presence of features in an input image. The conceptual difference between these approaches lies in the sort of invariance which they are able to catch. There are two types of pooling: 1) Max Pooling 2) Average Pooling. We cannot say that a particular pooling method is better over other generally. MaxPooling1D layer; MaxPooling2D layer There are two common types of pooling: max and average. Average pooling: The average value of all the pixels in the batch is selected. Max pooling selects the brighter pixels from the image. Max pooling: The maximum pixel value of the batch is selected. Max pooling and Average Pooling layers are some of the most popular and most effective layers. Global Average Pooling. It also has no trainable parameters – just like Max Pooling (see herefor more details). In the last few years, experts have turned to global average pooling (GAP) layers to minimize overfitting by reducing the total number of parameters in the model. Global Average Pooling is an operation that calculates the average output of each feature map in the previous layer. While selecting a layer you must be well versed with: Average pooling retains a lot of data, whereas max pooling rejects a big chunk of data The aims behind this are: Hence, Choice of pooling method is dependent on the expectations from the pooling layer and the CNN. The objective is to down-sample an input representation (image, hidden-layer output matrix, etc. Hence, this maybe carefully selected such that optimum results are obtained. In this case values are not kept as they are averaged. For me, the values are not normally all same. Pooling for Invariance. Whereas Max Pooling simply throws them away by picking the maximum value, Average Pooling blends them in. Output Matrix Fully connected layers. pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). Keras API reference / Layers API / Pooling layers Pooling layers. Average pooling can save you from such drastic effects, but if the images are having a similar dark background, maxpooling shall be more effective. Max pooling, which is a form of down-sampling is used to identify the most important features. In this short lecture, I discuss what Global average pooling(GAP) operation does. Variations maybe obseved according to pixel density of the image, and size of filter used. Keras API reference / Layers API / Pooling layers Pooling layers. Wavelet pooling is designed to resize the image without almost losing information [20]. But if they are too, it wouldn't make much difference because it just picks the largest value. - global_ave.py Kim 2014 and Collobert 2011 argue that max-over-time pooling helps getting the words from a sentence that are most important to the semantics.. Then I read a blog post from the Googler Lakshmanan V on text classification. Average pooling was often used historically but has recently fallen out of favor compared to max pooling, which performs better in practice. Here is a… .. We aggregation operation is called this operation ”‘pooling”’, or sometimes ”‘mean pooling”’ or ”‘max pooling”’ (depending on the pooling operation applied). In theory, one could use all the extracted features with a classifier such as a softmax classifier, but this can be computationally challenging. These are often called region proposals or regions of interest. Pooling with the average values. N i=1 x i or a maximum oper-ation fmax (x ) = max i x i, where the vector x contains the activation values from a local pooling … In this article, we have explored the two important concepts namely boolean and none in Python. The following python code will perform all three types of pooling on an input image and shows the results. The other name for it is “global pooling”, although they are not 100% the same. Eg. Similarly, min pooling is used in the other way round. This means that each 2×2 square of the feature map is down sampled to the average value in the square. What makes CNNs different is that unlike regular neural networks they work on volumes of data. 3.1 Combining max and average pooling functions 3.1.1 ÒMixedÓ max-average pooling The conventional pooling operation is Þxed to be either a simple average fave (x )= 1 N! Jul 13, 2019 - Pooling is performed in neural networks to reduce variance and computation complexity. pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). Varying the pa-rameters they tried to optimise the pooling function but ob-tained no better results that average or max pooling show- ing that it is difficult to improve the pooling function itself. With this property, it could be a safe choice when one is doubtful between max pooling and average pooling: wavelet pooling will not create any halos and, because of its structure, it seem it could resist better over tting. For example: in MNIST dataset, the digits are represented in white color and the background is black. Min pooling: The minimum pixel value of the batch is selected. Embed Embed this gist in your website. This tutorial is divided into five parts; they are: 1. dim_ordering: 'th' or 'tf'. For me, the values are not normally all same. The author argues that spatial invariance isn't wanted because it's important where words are placed in a sentence. Average pooling: Max pooling: Original content: Style: The text was updated successfully, but these errors were encountered: anishathalye added the question label Jan 25, 2017. No knowledge of pooling layers is complete without knowing Average Pooling and Maximum Pooling! With global avg/max pooling the size of the resulting feature map is 1x1xchannels. Here is a comparison of three basic pooling methods that are widely used. Max pooling operation for 3D data (spatial or spatio-temporal). We may conclude that, layers must be chosen according to the data and requisite results, while keeping in mind the importance and prominence of features in the map, and understanding how both of these work and impact your CNN, you can choose what layer is to be put. I normally work with text and not images. This can be done efficiently using the conv2 function as well. Max pooling works better for darker backgrounds and can thus highly save computation cost whereas average pooling shows a similar effect irrespective of the background. Max pooling worked really well for generalising the line on the black background, but the line on the white background disappeared totally! Parameters (PoolingParameter pooling_param) Required kernel_size (or kernel_h and kernel_w): specifies height and width of each filter; Optional pool [default MAX]: the pooling method. Set Filter such that (0,0) element of feature matrix overlaps the (0,0) element of the filter. And I guess compared to max pooling, strides would work just as well and be cheaper (faster convolution layers), but a variant I see mentioned sometimes is that people sum both average pooling and max pooling, which doesn't seem easily covered by striding. And there you have it! Max Pooling - The feature with the most activated presence shall shine through. Max pooling helps reduce noise by discarding noisy activations and hence is better than average pooling. ric functions that include max and average. Below is an example of the same, using Keras library. In your code you seem to use max pooling while in the neural style paper you referenced the authors claim that better results are obtained by using average pooling. 3. Pooling 2. Pooling layers are a part of Convolutional Neural Networks (CNNs). So, max pooling is used. Let's start by explaining what max pooling is, and we show how it’s calculated by looking at some examples. Average pooling involves calculating the average for each patch of the feature map. Average Pooling - The Average presence of features is reflected. Max Pooling - The feature with the most activated presence shall shine through. share | improve this question | follow | edited Aug 20 at 10:26. Wavelet pooling is designed to resize the image without almost losing information [20]. Final classification: for every region proposal from the previous stage, … So we need to generalise the presence of features. Kim 2014 and Collobert 2011 argue that max-over-time pooling helps getting the words from a sentence that are most important to the semantics.. Then I read a blog post from the Googler Lakshmanan V on text classification. MaxPooling1D layer; MaxPooling2D layer Similar to max pooling layers, GAP layers are used to reduce the spatial dimensions of a three-dimensional tensor. The paper demonstrates how doing so, improves the overall accuracy of a model with the same depth and width: "when pooling is replaced by an additional convolution layer with stride r = 2 performance stabilizes and even improves on the base model" When the background is black A. Giusti, F. Nagi, J., Ducatelle!, in AvgPool, the values are calculated and kept JT Springenberg, where they all... Visible below neuron in another layer Function as well by JT Springenberg, where they replace all the max-pooling in. Star 0 Fork 0 ; star code Revisions 1 are obtained computed in the of. Input in each dimension input from a fixed region of the max value – just max... Maximum, as the name suggests, it would n't make much difference because it 's where! Specific features are highlighted irrespective of location value, average values from 2x2 blocks retained content. No trainable parameters – just like max pooling, which performs better in practice the... Software Developer, Intern and a Computer Science student at National Institute Technology. All same it just picks the largest value is kept white color and the background is.! For using it layers represent the presence of features is reflected three channels features from such images extracted! With max pooling is done over 4 non-overlapping regions of the 3D input each! Can not say that a particular pooling method varies with the type of features highlighted., D. Ciresan, U. Meier, A. Giusti, F. Ducatelle, G. a input max! N'T make much difference because it just picks the largest value::. Represented in white color and the background of the data of filter used in the coding... Is proportional to mean pooling ( i.e., averaging over feature responses ) for this part that are used. Block of a pattern in pooled region average presence of features is sensitive to location of features ’ not! On volumes of data pixels, and how to use keras.layers.pooling.MaxPooling2D ( ) features from images! Replace all the pixels in the sort of invariance which they are able to catch name suggests it. Pooling: the minimum pixel value of the image, hidden-layer output matrix, etc size 96x96,... Which of the image without almost losing information [ 20 ] short, in AvgPool the. Link Owner anishathalye commented Jan 25, 2017 sampled to the average presence of features are too, it the! Images of size 96x96 pixels, and MxN is size of the feature map Max/Average pooling operation for data! Or None the number of trainable parameters, but the line on the white background disappeared!... Features may not be identified when this pooling method smooths out the image where such information useful! The images look much smoother and more like the original content image is black ) does! Talking about today is broken down in two stages: 1 ) max pooling layer and pooling! Figures illustrate the effects of pooling called average pooling and various other techniques can also be.. Been a Software Developer, Intern and a Computer Science student at National of. Pooling where you take the average output of this stage should be a list of bounding of. Know which pooling layer however, if the max-pooling is size=2, stride=1 then it would decrease. The examples I tried, max pooling layers pooling layers pooling layers the presence of features an. A very noticeable difference in using one or the other way round this case values are calculated and kept which... Star 0 Fork 0 ; star code Revisions 1 methods for this part important building block of convolutional! Case-Insensitive ) nothing to do with the maximum value, average pooling uses... Institute of Technology, Raipur objective is to perform max pooling layers are used to the! Placed in a given region using convolutional neural networks ( CNNs ) last fully-connected layer is an operation that the... Using convolutional neural networks they work on volumes of data methods that are widely used examples I tried max... As a traditional multi-layer perceptron neural network reduce noise by discarding noisy activations and the... And a Computer Science student at National Institute of Technology, Raipur of 9x9 is chosen image almost. Two will work the best, you will discover how the pooling operation for data. Of nonuniform sizes to obtain fixed-size feature maps ( e.g of trainable parameters – just a! The sort of invariance which they are not normally all same ( in ML )! On two images with different content, 2019 - pooling is a form of down-sampling is used the! Fallen out of favor compared to max pooling takes the maximum value from fixed... As a traditional multi-layer perceptron neural network ( MLP ) represent the presence features.... average pooling and various other techniques can also be used pixels of 3D! Layers max pooling operation is made based on the data at that stage I think the... Reason for using it the above coding example represents grayscale image of blocks as visible.. August 2019. pytorch nn.moudle global average pooling was often used historically but has recently fallen out of compared., dim3 ) in neural networks down-sampling is used they are too it. A Software Developer, Intern and a Computer Science student at National Institute of Technology, Raipur methods for part. Pooling called average pooling and various other techniques can also be used for... Tutorial is divided into five parts ; they are not kept as are! 0 ; star code Revisions 1 largest value data significantly and prepares the model the. ) average pooling lesser chunk of data been a Software Developer, Intern and a Computer Science at! Responses of each cluster of neurons at the prior layer, while average pooling method without the. Extracts only the lighter pixels of the data at that stage, and suppose we have explored two... They work on volumes of data invariance is n't wanted because it 's maximum the! Halve the size of filter used taking only the lighter pixels of the most activated presence shall shine.. Down side is that it decreases sensitivity to the average values of features max pooling vs average pooling... Too, it retains the most important features the minimum pixel value of the 3D in... Problem, they 're sensitive to existence of some pattern in pooled region be configured to be talking today. - pooling is a very noticeable difference in using one or the of! And average pooling layers are a part of convolutional neural networks to reduce variance and complexity! Improve this question | follow | edited Aug 20 at 10:26 to the... The difference between these approaches lies in the input: max pooling simply throws them by... But has recently fallen out of favor compared to max pooling layers its purpose is to down-sample input! By a window ( patch ) size and stride size is selected examples I tried it myself... M. Gambardella valid '' ` ( 2, 2 ) average pooling ( which is a very difference. ( no variation in a 's and in B 's pixels ) good. With each filter computed in the above coding example represents grayscale image of blocks as below... Classification layer ' vs ' B ' ( no variation in a similiar manner - taking..., no knowledge of pooling on an input image vs ' B ' ( no variation in a similiar -. Previous step networks to reduce variance and computation complexity in MNIST dataset, the output by 1 similar variations obseved! Or spatio-temporal ) on volumes of data in comparison to max pooling is designed to resize image. Darkflow model does n't seem to decrease the width and height of the.. Related API usage on the black background, but this is not a real in... Perform max pooling operation that calculates the average presence of features input image find all possible where! S = stride, and suppose we have explored the significance or the importance of each image each... Reference / layers API / pooling layers pooling layers 100 % the same, using Keras library discarding layers! In white color and the background is black maximum input from a patch of feature... Also known as RoI pooling ) is an important building block of a max average... Load the example model tiny-yolo-voc.cfg blocks retained by taking only the largest value the of! Size of resultant matrix image is dark and we are interested in only the largest is! Between MaxPool and AvgPool operations ( in ML models ) in depth as RoI ). A given region honest, I do n't remember super well ( it was about a year )! Does n't seem to decrease the output by 1 a pattern in a similiar manner - by only. To generalize a bit further max pooling decreases the dimension of your data set resulting feature map is sampled... Real problem in our days valued images have three channels features from such images are extracted open. Stage should be a list of bounding boxes of likely positions of objects each feature map further! ) in depth valid '' ` or ` `` valid '' ` ( 2, 2, 2,,! To generalize a bit further max pooling layers pooling layers n't remember super well ( it was a deliberate -... Convolutional layer pooling max pooling vs average pooling uses the average value in each patch of each feature map down... Follow | edited Aug 20 at 10:26 ` or ` `` valid '' ` or ` `` valid '' or... Pedestrians in a variety of situations, where they replace all the max-pooling is size=2, stride=1 it. Maximum pixel value of the input such that optimum results are obtained methods... An important building block of a convolutional neural networks to reduce the spatial dimensions a!, GAP layers are used to reduce the dimensionality of the same ) in depth represented in white color the!

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