Task: Build CNN Model (preferably Keras or TensorFlow) to Predict Labels Associated to Each Image in CelebA Dataset (Multi-label Image Classification)
In past, for majority of multiclass/binary image classification problems, I used to feed images efficiently using
.flow_from_directory in Keras after images are properly organized in a separate directory for each class. Therefore, I have never bothered converting images to numpy.arrary prior to feeding to the model, unless I had to and of course datasets were small so that I could do it easily in my local machine.
However, CelebA is a Multi-label Image Classification with each images having 40 labels (attributes like Smiling, Eyeglasses, Young etc.) meaning that I can not organize them in subclasses as I used to do, so
.flow_from_directory is off the table (as far as I know!). Still I've managed to convert the images to numpy.array by the following simple loop:
import numpy as np import skimage.transform images_path='../img_align_celeba/' train_images= from skimage import data for filename in train['Images'].tolist()): tmp=np.array(skimage.transform.resize(io.imread(os.path.join(images_path,filename))/255., (64, 64))) train_images.append(tmp) x_train=np.array(train_images) del train_images
Well it was not an impossible task. CelebA dataset is large, well not super large compared to many other image datasets (>200K RGB images, totally 1.4GB in size, each image ~ 8 KB). YET surprisingly it takes the hell of the time to convert these images to numpy arrays and even stuck during the run of a small CNN model.
My computer specs: MacBook Pro (2015), Memory: 8GB, Harddisk: 128 GB.
Even with almost more than 4GM free memory, and 20 GB free hard-disk I could not manage this on my local machine.
UPDATE: It seems quite possible and way more efficient via
.flow_from_directory method of
ImageDataGenerator in Keras. While it is not that option for this multi label classification at hand, I simply made some dummy subclasses and it worked and the model runs much faster!!
To My Questions (finally!!):
Maybe I am not doing the image-to-numpy conversion efficiently? Please advice how I could construct the arrays in a more efficient approach!
UPDATE: I found a very similar question in stackoverflow a year ago, yet answers do not seem to offer any better alternative.
Maybe it is what it is and I only need better hardware to do it locally!?
How then Keras manage to do the conversion efficiently under the hood then?
At the end (as we speak), I sampled only 20% of the images to have at least a model prototype up and running, although accuracy is not impressive!!