I’m now running Wide ResNet on CIFAR dataset (CIFAR-10 and CIFAR-100). One confusing thing for me is that if I did data pre-processing (random horizontally flip and random crop), the test accuracy is much better than the accuracy that I used just normalized raw data. The code of pre-processing is shown below: def train_prep(x, y): ..
I am new in programming. I cloned the repository from Github. It works for the command python rotate.py -i group-airplanes.jpg -a 25. However, I want to rotate multiple images from a folder containing labels and images. How should I do that? Source: Python..
For the training of my model I want to perform data augmentation on the data set to improve performance. My input data instances consist of snapshots of wave height, saved in CSV (a 160×160 grid), which is the same for my labels/reference data. For that reason I want to perform the same data augmentation procedure ..
So I have done data augmentation in a keras model. I am using Fashin_Mnist dataset. Everything goes okay but when it finished the first epoch it trows an error. The error: ValueError: Shapes (32, 1) and (32, 10) are incompatible My data: img_rows = 28 img_cols = 28 batch_size = 512 img_shape = (img_rows, img_cols, ..
Good day, I am trying to implement a github repo specAugment (https://github.com/DemisEom/SpecAugment) After loading the wav file using librosa, I believe it uses numPy reshape function to reshape the melspectrogram array, get Log scale melspectrogram by using power_to_db function and apply the data augmentation. My question is, is it possible to get a wav file ..
I have multiple .wav files on google drive on which I want to do data augmentation and save the new file in the same folder. e.g. folder_1 contains abc.wav and after adding noise, the file should be saved as abc_noised.wav so that I could use all the files later altogether as a dataset. Code to ..
I am using this code I found on the internet. I understand the parameters of the function itself but I am having problems in the last loop. When I set the break at 20, I was thinking it will make 20 images of every image found in my directory. My directory has about 420 images ..
I am working on Semantic segmentation using U-net and I’m trying to augment training data using ImageDataGenerator. There is one parameter whose effect I don’t completely understand – the parameter rounds in the .fit part shown below in the code. I have checked the Keras documentation (https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator#fit) and it says that rounds parameter does the ..
Preparing my data with ImageDataGenerator. So far I did the following, For training data: image_data_generator_training = tf.keras.preprocessing.image.ImageDataGenerator( **process** validation_split=0.2 ).flow_from_directory(‘./dataset/fg_image’, batch_size = 16, target_size = (224, 224), seed = SEED, subset=’training’) mask_data_generator_training = tf.keras.preprocessing.image.ImageDataGenerator( **process** validation_split=0.2 ).flow_from_directory(‘./dataset/gt_mask’, batch_size = 16, target_size = (224, 224), seed = SEED, subset=’training’) For validation data: image_data_generator_validation = tf.keras.preprocessing.image.ImageDataGenerator( **process** ..
I have a set of 5 sentinel 2 images gathered with 4 bands that I need to augment them to produce more training data. After splitting the data to train validate and test splits I took the train sets of for (21457,4,4) I tried to apply some augmentation techniques according to what researched made for ..