data-augmentation

问题 Say you have a dataset that has images and some data in a .csv for each image. Your goal is to create a NN that has a convolution branch and an other one (in my case an MLP). Now, there are plenty of guides (one here, another one) on how to create the network, that's not the problem. The issue here is how do I create an iterator in the form of [[convolution_input, other_features], target] when the convolution_input is from a Keras ImageDataGenerator flow that adds augmented images. More

问题 Say you have a dataset that has images and some data in a .csv for each image. Your goal is to create a NN that has a convolution branch and an other one (in my case an MLP). Now, there are plenty of guides (one here, another one) on how to create the network, that's not the problem. The issue here is how do I create an iterator in the form of [[convolution_input, other_features], target] when the convolution_input is from a Keras ImageDataGenerator flow that adds augmented images. More

问题 Say you have a dataset that has images and some data in a .csv for each image. Your goal is to create a NN that has a convolution branch and an other one (in my case an MLP). Now, there are plenty of guides (one here, another one) on how to create the network, that's not the problem. The issue here is how do I create an iterator in the form of [[convolution_input, other_features], target] when the convolution_input is from a Keras ImageDataGenerator flow that adds augmented images. More

问题 Iam using runtime data augmentation using generators in keras for segmentation problem.. Here is my data generator data_gen_args = dict( width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.2, horizontal_flip=True, validation_split=0.2 ) image_datagen = ImageDataGenerator(**data_gen_args) def generate_data_generator(generator, Xi, Yi): genXi = generator.flow(Xi, seed=7, batch_size=32) genYi = generator.flow(Yi, seed=7,batch_size=32) while True: Xi = genXi.next() Yi = genYi.next() print

问题 I'm following this guide. It shows how to download datasets from the new TensorFlow Datasets using tfds.load() method: import tensorflow_datasets as tfds SPLIT_WEIGHTS = (8, 1, 1) splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS) (raw_train, raw_validation, raw_test), metadata = tfds.load( 'cats_vs_dogs', split=list(splits), with_info=True, as_supervised=True) The next steps shows how to apply a function to each item in the dataset using map method: def format_example(image, label):

问题 I am working on MNIST dataset, in which X_train = (42000,28,28,1) is the training set. y_train = (42000,10) is the corresponding label set. Now I create an iterator from the image generator using Keras as follows; iter=datagen.flow(X_train,y_train,batch_size=32) which works fine. Then I train the model using; model.fit_generator(iter,steps_per_epoch=len(X_train)/32,epochs=1) Here it gives the following error; ValueError: Error when checking input: expected dense_9_input to have 2 dimensions,

问题 My problem I would like to use a kind of data-augmentation method for NLP consisting of back-translating dataset. Basically, I have a large dataset (SNLI), consisting of 1 100 000 english sentences. What I need to do is : translate these sentences in a language, and translate it back to English. I may have to do this for several language. So I have a lot of translations to do. I need a free solution. What I did so far I tried several python module for translation, but due to recent changes in

问题 How to use different data augmentation (transforms) for different Subset s in PyTorch? For instance: train, test = torch.utils.data.random_split(dataset, [80000, 2000]) train and test will have the same transforms as dataset . How to use custom transforms for these subsets? 回答1: My current solution is not very elegant, but works: from copy import copy train_dataset, test_dataset = random_split(full_dataset, [train_size, test_size]) train_dataset.dataset = copy(full_dataset) test_dataset

问题 I'm trying to adapt Deep Learning with Python section 5.3 Feature extraction with Data Augmentation to a 3-class problem with resnet50 (imagenet weights). Full code at https://github.com/morenoh149/plantdisease from keras import models from keras import layers from keras.applications.resnet50 import ResNet50 from keras import optimizers from keras.preprocessing.image import ImageDataGenerator input_shape = (224, 224, 3) target_size = (224, 224) batch_size = 20 conv_base = ResNet50(weights=

问题 I have 1000 resume in png format and I am implementing MaskRcnn for object detection. What data augmentation techniques can I use to improve the mask Rcnn performance? 回答1: I don't think MaskRCNN or other semantic segmentation or object detection tools are appropriate for this task. Look into OCR libraries such as Tesseract. 来源： https://stackoverflow.com/questions/58748719/what-are-some-good-data-augmentation-techniques-for-document-images