Why CIFAR-10 images are not displayed properly using matplotlib?

Following prints 5X5 grid of random Cifar10 images. It isn't blurry, though not perfect either. Any suggestions welcome.

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from six.moves import cPickle 

f = open('data/cifar10/cifar-10-batches-py/data_batch_1', 'rb')
datadict = cPickle.load(f,encoding='latin1')
f.close()
X = datadict["data"] 
Y = datadict['labels']
X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype("uint8")
Y = np.array(Y)

#Visualizing CIFAR 10
fig, axes1 = plt.subplots(5,5,figsize=(3,3))
for j in range(5):
    for k in range(5):
        i = np.random.choice(range(len(X)))
        axes1[j][k].set_axis_off()
        axes1[j][k].imshow(X[i:i+1][0])

Make sure you don't normalize your dataset when you want to display the image.

Example :

The loader...

import torch
from torchvision import datasets, transforms
import matplotlib.pyplot as plt


train_loader = torch.utils.data.DataLoader(
    datasets.CIFAR10('../data', train=True, download=True,
                     transform=transforms.Compose([
                         transforms.RandomHorizontalFlip(),
                         transforms.ToTensor(),
                        #  transforms.Normalize(
                        #      (0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))
                     ])),
    batch_size=64, shuffle=True)

The code that shows the image...

img = next(iter(train_loader))[0][0]
plt.imshow(transforms.ToPILImage()(img))

Normalized

Wihtout normalization

I made a function to plot the RGB image from a row in the CIFAR10 dataset.The image will be blurry at best since the original size of the image is very small (32px X 32px).

def unpickle(file):
    with open(file, 'rb') as fo:
        dict1 = pickle.load(fo, encoding='bytes')
    return dict1

pd_tr = pd.DataFrame()
tr_y = pd.DataFrame()

for i in range(1,6):
    data = unpickle('data/data_batch_' + str(i))
    pd_tr = pd_tr.append(pd.DataFrame(data[b'data']))
    tr_y = tr_y.append(pd.DataFrame(data[b'labels']))
    pd_tr['labels'] = tr_y

tr_x = np.asarray(pd_tr.iloc[:, :3072])
tr_y = np.asarray(pd_tr['labels'])
ts_x = np.asarray(unpickle('data/test_batch')[b'data'])
ts_y = np.asarray(unpickle('data/test_batch')[b'labels'])    
labels = unpickle('data/batches.meta')[b'label_names']

def plot_CIFAR(ind):
    arr = tr_x[ind]
    sc_dpi = 157.35
    R = arr[0:1024].reshape(32,32)/255.0
    G = arr[1024:2048].reshape(32,32)/255.0
    B = arr[2048:].reshape(32,32)/255.0

    img = np.dstack((R,G,B))
    title = re.sub('[!@#$b]', '', str(labels[tr_y[ind]]))
    fig = plt.figure(figsize=(3,3))
    ax = fig.add_subplot(111)
    ax.imshow(img,interpolation='bicubic')
    ax.set_title('Category = '+ title,fontsize =15)

plot_CIFAR(4)

code result is: Try below code.

I found a very useful link about visualization of mnist and cifar images. You can find codes for various images : https://machinelearningmastery.com/how-to-load-and-visualize-standard-computer-vision-datasets-with-keras/ cifar10 image code is below: It works well. Image is above.

# example of loading the cifar10 dataset
from matplotlib import pyplot
from keras.datasets import cifar10
# load dataset
(trainX, trainy), (testX, testy) = cifar10.load_data()
# summarize loaded dataset
print('Train: X=%s, y=%s' % (trainX.shape, trainy.shape))
print('Test: X=%s, y=%s' % (testX.shape, testy.shape))
# plot first few images
for i in range(9):
    # define subplot
    pyplot.subplot(330 + 1 + i)
    # plot raw pixel data
    pyplot.imshow(trainX[i])
# show the figure
pyplot.show()