![]() Return crops, minval = 0, maxval = len ( scales ), dtype = tf. zeros ( len ( scales )), crop_size = ( 32, 32 )) # Return a random crop crop_and_resize (, boxes = boxes, box_ind = np. zeros (( len ( scales ), 4 )) for i, scale in enumerate ( scales ): x1 = y1 = 0.5 - ( 0.5 * scale ) x2 = y2 = 0.5 + ( 0.5 * scale ) boxes = def random_crop ( img ): # Create different crops for an imageĬrops = tf. """ # Generate 20 crop settings, ranging from a 1% to 20% crop. These are only evaluated once in the TF data pipeline and will result in the same augmentation applied to all images.ĭef zoom ( x : tf. Note: Do not use np.random functions for generating random numbers in these augmenter functions. true_fn is set to the cropping function and false_fn to a identity function returning the original image. In our case we use a random number generator to return true in 50% of the calls. The predicate should be an operation that evaluates to true or false, after which true_fn or false_fn is called respectively. tf.cond expects three parameters: a predicate (or condition), a true function true_fn and a false function false_fn. To make sure that some part of our data retains it original dimension, a tf.cond call can be used. tf.random_uniform will give new random numbers during training so is safe to use here. By using tf.random_uniform we can randomly select one of these crops. This function returns a new image for each crop box, resulting in 20 potential cropped images for each input image. These boxes are created once and then passed on to the crop_and_resize function. In the augmentation function below we first create 20 crop boxes using numpy. The function requires a list of ‘crop boxes’ that contain normalized coordinates (between 0 and 1) for cropping. The Tensorflow function crop_and_resize function comes close as it can crop an image and then resize it to an arbitrary size. This augmentation is a bit harder to implement as there is no single function that performs this operation completely. Zooming is a powerful augmentation that can make a network robust to (small) changes in object size. random_contrast ( x, 0.7, 1.3 ) return x random_saturation ( x, 0.6, 1.6 ) x = tf. Random functions from Tensorflow are evaluated for every input, functions from numpy or basic python only once which would result in a static augmentation.ĭef color ( x : tf. To get a new random rotation for each image we need to use a random function from Tensorflow itself. For this we can use the rot90 function of Tensorflow. One of the most simplest augmentations is rotating the image 90 degrees. Of course, there are many more augmentations that could be useful, but most of them follow the same approach. Nevertheless, I show them here as an example as they can be useful for tasks that are more orientation invariant. learning to detect flipped trucks is maybe not that beneficial for the task at hand. Not all of these augmentations are necessarily applicable to CIFAR10 e.g. Color augmentations (hue, saturation, brightness, contrast).With this basic recipe for an augmenter function we can implement the augmenters itself. compile ( optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = 'accuracy' ) model. dense2 ( x ) return x model = MyModel () model. Dense ( 5, activation = 'softmax' ) def call ( self, xb ): x = self. Conv2D ( 64, ( 3, 3 ), activation = 'relu' ) self. Conv2D ( 32, ( 3, 3 ), activation = 'relu' ) self. Model ): def _init_ ( self ): super ( MyModel, self ). # Using Subclass API without transfer learning & Eager Execution class MyModel ( keras. Geometric transforms ()ĭomain adaptation transforms (augmentations.domain_adaptation)įunctional transforms (augmentations.functional) Helper functions for working with keypoints (_utils)Ĭrop functional transforms ()Ĭrop transforms ()ĬhannelDropout augmentation (_dropout)ĬoarseDropout augmentation (_dropout)Ĭutout augmentation () Helper functions for working with bounding boxes (_utils) Transforms Interface (ansforms_interface) ![]() Semantic segmentation on the Pascal VOC datasetĪlbumentations Experimental Transforms (ansforms)īlog posts, podcasts, talks, and videos about Albumentationsįrameworks and libraries that use Albumentations Image classification on the ImageNet dataset Image classification on the CIFAR10 dataset How to use a custom classification or semantic segmentation model Simultaneous augmentation of multiple targets: masks, bounding boxes, keypointsĪ list of transforms and their supported targetsīenchmarks and a comparison with baseline augmentation strategies Bounding boxes augmentation for object detection
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