Picture Augmentation for Deep Studying with Keras

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05d4 Final Up to date on 05d4 July 19, 2022

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05d4 Knowledge preparation is required when 05d4 working with neural community and 05d4 deep studying fashions. More and 05d4 more 05d4 information augmentation 05d4 can also be required 05d4 on extra complicated object recognition 05d4 duties.

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05d4 On this submit you’ll uncover 05d4 the best way to use 05d4 information preparation and information augmentation 05d4 together with your picture datasets 05d4 when creating and evaluating deep 05d4 studying fashions in Python with 05d4 Keras.

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05d4 After studying this submit, you’ll 05d4 know:

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    05d4

  • 05d4 In regards to the picture 05d4 augmentation API present by Keras 05d4 and the best way to 05d4 use it together with your 05d4 fashions.

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  • 05d4 How one can carry out 05d4 characteristic standardization.

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  • 05d4 How one can carry out 05d4 ZCA whitening of your photos.

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  • 05d4 How one can increase information 05d4 with random rotations, shifts and 05d4 flips.

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  • 05d4 How one can save augmented 05d4 picture information to disk.

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05d4

05d4 Kick-start your challenge 05d4 with my new guide 05d4 05d4 Deep Studying With Python 05d4 , together with 05d4 step-by-step tutorials 05d4 and the 05d4 Python supply code 05d4 recordsdata for all examples.

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05d4 Let’s get began.

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    05d4

  • 05d4 Jun/2016 05d4 : First revealed

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  • 05d4 Replace Aug/2016 05d4 : The examples on this 05d4 submit had been up to 05d4 date for the most recent 05d4 Keras API. The datagen.subsequent() operate 05d4 was eliminated.

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  • 05d4 Replace Oct/2016 05d4 : Up to date for 05d4 Keras 1.1.0, TensorFlow 0.10.0 and 05d4 scikit-learn v0.18.

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  • 05d4 Replace Jan/2017 05d4 : Up to date for 05d4 Keras 1.2.0 and TensorFlow 0.12.1.

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  • 05d4 Replace Mar/2017 05d4 : Up to date for 05d4 Keras 2.0.2, TensorFlow 1.0.1 and 05d4 Theano 0.9.0.

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  • 05d4 Replace Sep/2019 05d4 : Up to date for 05d4 Keras 2.2.5 API.

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  • 05d4 Replace Jul/2022 05d4 : Up to date for 05d4 TensorFlow 2.x API with workaround 05d4 on the characteristic standardization problem

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05d4 For an prolonged tutorial on 05d4 the ImageDataGenerator for picture information 05d4 augmentation, see:

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05d4 Keras Picture Augmentation API

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05d4 Like the remainder of Keras, 05d4 the picture augmentation API is 05d4 easy and highly effective.

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05d4 Keras supplies the 05d4 ImageDataGenerator 05d4 class that defines the 05d4 configuration for picture information preparation 05d4 and augmentation. This contains capabilities 05d4 comparable to:

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  • 05d4 Pattern-wise standardization.

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  • 05d4 Function-wise standardization.

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  • 05d4 ZCA whitening.

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  • 05d4 Random rotation, shifts, shear and 05d4 flips.

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  • 05d4 Dimension reordering.

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  • 05d4 Save augmented photos to disk.

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05d4 An augmented picture generator could 05d4 be created as follows:

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05d4 Moderately than performing the operations 05d4 in your complete picture dataset 05d4 in reminiscence, the API is 05d4 designed to be iterated by 05d4 the deep studying mannequin becoming 05d4 course of, creating augmented picture 05d4 information for you just-in-time. This 05d4 reduces your reminiscence overhead, however 05d4 provides some extra time price 05d4 throughout mannequin coaching.

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05d4 After you might have created 05d4 and configured your 05d4 ImageDataGenerator 05d4 , you will need to 05d4 match it in your information. 05d4 It will calculate any statistics 05d4 required to really carry out 05d4 the transforms to your picture 05d4 information. You are able to 05d4 do this by calling the 05d4 05d4 match() 05d4 operate on the information 05d4 generator and move it your 05d4 coaching dataset.

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05d4 The information generator itself is 05d4 in truth an iterator, returning 05d4 batches of picture samples when 05d4 requested. We are able to 05d4 configure the batch measurement and 05d4 put together the information generator 05d4 and get batches of photos 05d4 by calling the 05d4 circulation() 05d4 operate.

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05d4 Lastly we are able to 05d4 make use of the information 05d4 generator. As a substitute of 05d4 calling the 05d4 match() 05d4 operate on our mannequin, 05d4 we should name the 05d4 fit_generator() 05d4 operate and move within 05d4 the information generator and the 05d4 specified size of an epoch 05d4 in addition to the whole 05d4 variety of epochs on which to 05d4 coach.

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05d4 You may be taught extra 05d4 in regards to the Keras 05d4 picture 05d4 information generator API within the 05d4 Keras documentation 05d4 .

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05d4 Need assistance with Deep Studying 05d4 in Python?

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05d4 Take my free 2-week e-mail 05d4 course and uncover MLPs, CNNs 05d4 and LSTMs (with code).

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05d4 Click on to sign-up now 05d4 and in addition get a 05d4 free PDF E-book model of 05d4 the course.

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05d4 05d4

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05d4 Level of Comparability for Picture 05d4 Augmentation

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05d4 Now that you know the 05d4 way the picture augmentation API 05d4 in Keras works, let’s take 05d4 a look at some examples.

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05d4 We are going to use 05d4 the 05d4 MNIST handwritten digit recognition activity 05d4 in these examples. To 05d4 start with, let’s check out 05d4 the primary 9 photos within 05d4 the coaching dataset.

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05d4 Working this instance supplies the 05d4 next picture that we are 05d4 able to use as some 05d4 extent of comparability with the 05d4 picture preparation and augmentation within 05d4 the examples under.

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05d4 Instance MNIST photos

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05d4 Function Standardization

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05d4 It’s also doable to standardize 05d4 pixel values throughout the complete 05d4 dataset. That is referred to 05d4 as characteristic standardization and mirrors 05d4 the kind of standardization typically 05d4 carried out for every column 05d4 in a tabular dataset.

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05d4 You may carry out characteristic 05d4 standardization by setting the 05d4 featurewise_center 05d4 and 05d4 featurewise_std_normalization 05d4 arguments to True on 05d4 the 05d4 ImageDataGenerator 05d4 class. These are set 05d4 to False by default. Nevertheless, 05d4 the latest model of Keras 05d4 has a bug on the 05d4 characteristic standardization that the imply 05d4 and commonplace deviation is calculated 05d4 throughout all pixels. If you 05d4 happen to use the 05d4 match() 05d4 operate from the 05d4 ImageDataGenerator 05d4 class, you will notice 05d4 a picture just like the 05d4 one above:

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05d4 The minimal, imply, and most 05d4 worth from the batch printed 05d4 above is, for instance:

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05d4 and the picture displayed is 05d4 as follows:

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05d4 Picture from featurewise standardization

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05d4 The workaround is to compute 05d4 the characteristic standardization manually. Every 05d4 pixel ought to have a 05d4 separate imply and commonplace deviation 05d4 and it ought to be 05d4 computed throughout completely different samples 05d4 however unbiased from different pixels 05d4 in the identical pattern. We 05d4 simply want to interchange the 05d4 05d4 match() 05d4 operate with our personal 05d4 computation:

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05d4 The minimal, imply, and most 05d4 as printed is now having 05d4 a wider vary:

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05d4 Working this instance you’ll be 05d4 able to see that the 05d4 impact is completely different, seemingly 05d4 darkening and lightening completely different 05d4 digits.

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05d4 Standardized Function MNIST Pictures

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05d4 ZCA Whitening

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05d4 A 05d4 whitening rework 05d4 of a picture is 05d4 a linear algebra operation that 05d4 reduces the redundancy within the 05d4 matrix of pixel photos.

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05d4 Much less redundancy within the 05d4 picture is meant to raised 05d4 spotlight the buildings and options 05d4 within the picture to the 05d4 educational algorithm.

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05d4 Usually, picture whitening is carried 05d4 out utilizing the Principal Part 05d4 Evaluation (PCA) approach. Extra not 05d4 too long ago, another referred 05d4 to as 05d4 ZCA 05d4  ( 05d4 be taught extra in Appendix 05d4 A of this tech report 05d4 ) reveals higher outcomes and 05d4 leads to remodeled photos that 05d4 retains the entire authentic dimensions 05d4 and in contrast to PCA, 05d4 ensuing remodeled photos nonetheless appear 05d4 like their originals. Exactly, whitening 05d4 is to transform every picture 05d4 right into a white noise 05d4 vector, i.e., every ingredient within 05d4 the vector has zero imply, 05d4 unit commonplace derivation, and statistically 05d4 unbiased of one another.

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05d4 You may carry out a 05d4 ZCA whitening rework by setting 05d4 the 05d4 zca_whitening 05d4 argument to True. However 05d4 as a result of identical 05d4 problem as characteristic standardization, we 05d4 should first zero-center our enter 05d4 information individually:

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05d4 Working the instance, you’ll be 05d4 able to see the identical 05d4 basic construction within the photos 05d4 and the way the define 05d4 of every digit has been 05d4 highlighted.

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05d4 ZCA Whitening MNIST Pictures

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05d4 Random Rotations

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05d4 Generally photos in your pattern 05d4 information might have various and 05d4 completely different rotations within the 05d4 scene.

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05d4 You may prepare your mannequin 05d4 to raised deal with rotations 05d4 of photos by artificially and 05d4 randomly rotating photos out of 05d4 your dataset throughout coaching.

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05d4 The instance under creates random 05d4 rotations of the MNIST digits 05d4 as much as 90 levels 05d4 by setting the rotation_range argument.

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05d4 Working the instance, you’ll be 05d4 able to see that photos 05d4 have been rotated left and 05d4 proper as much as a 05d4 restrict of 90 levels. This 05d4 isn’t useful on this drawback 05d4 as a result of the 05d4 MNIST digits have a normalized 05d4 orientation, however this rework may 05d4 be of assist when studying 05d4 from images the place the 05d4 objects might have completely different 05d4 orientations.

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05d4 Random Rotations of MNIST Pictures

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05d4 Random Shifts

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05d4 Objects in your photos might 05d4 not be centered within the 05d4 body. They could be off-center 05d4 in a wide range of 05d4 other ways.

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05d4 You may prepare your deep 05d4 studying community to anticipate and 05d4 presently deal with off-center objects 05d4 by artificially creating shifted variations 05d4 of your coaching information. Keras 05d4 helps separate horizontal and vertical 05d4 random shifting of coaching information 05d4 by the 05d4 width_shift_range 05d4 and 05d4 height_shift_range 05d4 arguments.

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05d4 Working this instance creates shifted 05d4 variations of the digits. Once 05d4 more, this isn’t required for 05d4 MNIST because the handwritten digits 05d4 are already centered, however you’ll 05d4 be able to see how 05d4 this may be helpful on 05d4 extra complicated drawback domains.

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05d4 Random Shifted MNIST Pictures

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05d4 Random Flips

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05d4 One other augmentation to your 05d4 picture information that may enhance 05d4 efficiency on massive and complicated 05d4 issues is to create random 05d4 flips of photos in your 05d4 coaching information.

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05d4 Keras helps random flipping alongside 05d4 each the vertical and horizontal 05d4 axes utilizing the 05d4 vertical_flip 05d4 and 05d4 horizontal_flip 05d4 arguments.

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05d4 Working this instance you’ll be 05d4 able to see flipped digits. 05d4 Flipping digits just isn’t helpful 05d4 as they may at all 05d4 times have the proper left 05d4 and proper orientation, however this 05d4 can be helpful for issues 05d4 with images of objects in 05d4 a scene that may have 05d4 a diversified orientation.

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05d4 Randomly Flipped MNIST Pictures

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05d4 Saving Augmented Pictures to File

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05d4 The information preparation and augmentation 05d4 is carried out simply in 05d4 time by Keras.

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05d4 That is environment friendly by 05d4 way of reminiscence, however it’s 05d4 possible you’ll require the precise 05d4 photos used throughout coaching. For 05d4 instance, maybe you want to 05d4 use them with a unique 05d4 software program bundle later or 05d4 solely generate them as soon 05d4 as and use them on 05d4 a number of completely different 05d4 deep studying fashions or configurations.

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05d4 Keras means that you can 05d4 save the pictures generated throughout 05d4 coaching. The listing, filename prefix 05d4 and picture file kind could 05d4 be specified to the 05d4 circulation() 05d4 operate earlier than coaching. 05d4 Then, throughout coaching, the generated 05d4 photos shall be written to 05d4 file.

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05d4 The instance under demonstrates this 05d4 and writes 9 photos to 05d4 a “ 05d4 photos 05d4 ” subdirectory with the prefix 05d4 “ 05d4 aug 05d4 ” and the file kind 05d4 of PNG.

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05d4 Working the instance you’ll be 05d4 able to see that photos 05d4 are solely written when they’re 05d4 generated.

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Augmented MNIST Images Saved To File

05d4 Augmented MNIST Pictures Saved To 05d4 File

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05d4 Ideas For Augmenting Picture Knowledge 05d4 with Keras

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05d4 Picture information is exclusive in 05d4 you could evaluation the information 05d4 and remodeled copies of the 05d4 information and rapidly get an 05d4 thought of how the mannequin 05d4 could also be understand it 05d4 by your mannequin.

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05d4 Under are some ideas for 05d4 getting probably the most from 05d4 picture information preparation and augmentation 05d4 for deep studying.

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  • 05d4 Evaluation Dataset 05d4 . Take a while to 05d4 evaluation your dataset in nice 05d4 element. Take a look at 05d4 the pictures. Be aware of 05d4 picture preparation and augmentations that 05d4 may profit the coaching strategy 05d4 of your mannequin, comparable to 05d4 the necessity to deal with 05d4 completely different shifts, rotations or 05d4 flips of objects within the 05d4 scene.

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  • 05d4 Evaluation Augmentations 05d4 . Evaluation pattern photos after 05d4 the augmentation has been carried 05d4 out. It’s one factor to 05d4 intellectually know what picture transforms 05d4 you’re utilizing, it’s a very 05d4 completely different factor to take 05d4 a look at examples. Evaluation 05d4 photos each with particular person 05d4 augmentations you’re utilizing in addition 05d4 to the complete set of 05d4 augmentations you intend to make 05d4 use of. You may even 05d4 see methods to simplify or 05d4 additional improve your mannequin coaching 05d4 course of.

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  • 05d4 Consider a Suite of Transforms 05d4 . Strive a couple of 05d4 picture information preparation and augmentation 05d4 scheme. Typically you could be 05d4 stunned by outcomes of a 05d4 knowledge preparation scheme you didn’t 05d4 suppose could be useful.

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05d4

05d4 Abstract

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05d4 On this submit you found 05d4 picture information preparation and augmentation.

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05d4 You found a variety of 05d4 methods that you should use 05d4 simply in Python with Keras 05d4 for deep studying fashions. You 05d4 realized about:

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  • 05d4 The ImageDataGenerator API in Keras 05d4 for producing remodeled photos simply 05d4 in time.

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  • 05d4 Pattern-wise and Function sensible pixel 05d4 standardization.

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  • 05d4 The ZCA whitening rework.

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  • 05d4 Random rotations, shifts and flips 05d4 of photos.

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  • 05d4 How one can save remodeled 05d4 photos to file for later 05d4 reuse.

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05d4

05d4 Do you might have any 05d4 questions on picture information augmentation 05d4 or this submit? Ask your 05d4 questions within the feedback and 05d4 I’ll do my greatest to 05d4 reply.

05d4

05d4

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