Keras image data generator generator#
Right from the MNIST dataset which has just 60k training images to the ImageNet dataset with over 14 million images a data generator would be an invaluable tool for deep learning training as well as inference. What my experience in both of these roles has taught me so far is that one cannot overemphasize the importance of data generators for training. I have worked as an academic researcher and am currently working as a research engineer in the Industry. Take note that given your directory structure, it looks like you are performing a binary segmentation per image, so that's why I chose binary cross-entropy as the loss function.How to effectively and efficiently use data generators in Keras for Computer Vision applications of Deep Learning pile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = )
My_generator = my_image_mask_generator(image_data_generator, mask_data_generator) # Create custom generator for training images and masks Assuming you've already constructed the model correctly: from keras.optimizers import Adam Next, create your data generators as you did normally: SEED = 100įinally, call the fit_generator method on your model. Train_generator = zip(image_data_generator, mask_data_generator) Therefore, do something like this: def my_image_mask_generator(image_data_generator, mask_data_generator): Make sure you do that before we proceed here. The reason is that even though you match the seeds, it will randomly choose the filenames before loading the images in memory, so to make sure that the same order of selection is maintained among the training images and corresponding masks, their filenames also need to match. For example, if you have a training image called 1.tif in your rendered_imges/render subdirectory, you will need to name your corresponding mask the same way in ground_truths/mask. One final thing I need to mention is that the filenames for both directories need to match if you want to successfully pair the image and corresponding mask.
Keras image data generator zip#
In summary, you will simply take the two ImageDataGenerators that you created, zip them together, then have a loop that will yield each batch of training images and expected output labels. Therefore, create a new function that will output a collection of tuples that give you the image and the mask. This is of course no longer the case when you're trying to do semantic segmentation. By simply using the data generator on its own, the sub-directories will implicitly encode the expected label of the image. Specifically, the way fit_generator works is that yields a sequence of tuples such that the first element of the tuple is the image and the second element of the tuple is the expected output. You will need to make a new function that will generate both the training image and corresponding mask that you will use to feed into the fit_generator method. It would be great if someone can suggest a way to do this properly using Keras or Tensorflow. With the above approach although I am getting the same augmentations applied to both image and mask, the images are not getting paired with their respective masks according to their filenames.
).flow_from_directory('./new/ground_truths', batch_size = 16, target_size = (150, 150), seed = SEED) Mask_data_generator = ImageDataGenerator( ).flow_from_directory('./new/rendered_images', batch_size = 16, target_size = (150, 150), seed = SEED) Image_data_generator = ImageDataGenerator( My approach was the following: SEED = 100 I tried writing an ImageDataGenerator for augmenting both the images and their respective masks in exactly the same way. In the above directory structure, image_.tif. The dataset that I am using has the images and masks stored in separate directories and each filename has is an id for mapping an image file with its respective mask.įollowing is the structure of my dataset directory: new I am trying to build a semantic segmentation model using tensorflow.keras.
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