![]() ![]() uniform (( 1 ,), minval = 0, maxval = IMG_SIZE ) # ry cut_y = keras. uniform (( 1 ,), minval = 0, maxval = IMG_SIZE ) # rx cut_x = keras. cast ( cut_w, "int32" ) cut_h = IMG_SIZE * cut_rat # rh cut_h = keras. sqrt ( 1.0 - lambda_value ) cut_w = IMG_SIZE * cut_rat # rw cut_w = keras. gamma ( shape =, alpha = concentration_0 ) return gamma_1_sample / ( gamma_1_sample + gamma_2_sample ) def get_box ( lambda_value ): cut_rat = keras. gamma ( shape =, alpha = concentration_1 ) gamma_2_sample = tf_random. We then crop the second image ( image2)Īnd pad this image in the final padded image at the same location.ĭef sample_beta_distribution ( size, concentration_0 = 0.2, concentration_1 = 0.2 ): gamma_1_sample = tf_random. It samples λ(l) from the Beta distributionĪnd returns a bounding box from get_box function. The CutMix function takes two image and label pairs to perform the augmentation. prefetch ( AUTO ) )ĭefine the CutMix data augmentation function map ( preprocess_image, num_parallel_calls = AUTO ). ![]() zip (( train_ds_one, train_ds_two )) test_ds = ( test_ds. prefetch ( AUTO ) ) # Combine two shuffled datasets from the same training data. from_tensor_slices (( x_test, y_test )) train_ds_simple = ( train_ds_simple. from_tensor_slices (( x_train, y_train )) test_ds = tf_data. map ( preprocess_image, num_parallel_calls = AUTO ) ) train_ds_simple = tf_data. from_tensor_slices (( x_train, y_train )). map ( preprocess_image, num_parallel_calls = AUTO ) ) train_ds_two = ( tf_data. Where rx, ry are randomly drawn from a uniform distribution with upper bound. The bounding box sampling is represented by: Which indicates the cutout and fill-in regions in case of the images. Regions from the two randomly drawn images and λ (in ) is drawn from a Where M is the binary mask which indicates the cutout and the fill-in It's implemented via the following formulas: While the ground truth labels are mixed proportionally to the number of pixels of combined images.ĬutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features You replace the removed regions with a patch from another image, Instead of removing pixels and filling them with black or grey pixels or Gaussian noise, CutMix is a data augmentation technique that addresses the issue of information lossĪnd inefficiency present in regional dropout strategies. ![]()
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