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Image_classification_of_CIFAR10_dataset

The CIFAR-10 dataset contains 60,000 color images of 32 x 32 pixels in 3 channels divided into 10 classes. Each class contains 6,000 images. The training set contains 50,000 images, while the test sets provides 10,000 images.This is a classification problem with 10 classes(muti-label classification). We can take a view on this image for more comprehension of the dataset.

CIFAR10_categories

More about the dataset

More details can be found in the below link. https://www.cs.toronto.edu/~kriz/cifar.html

Data lables using Categorical Encoding

The output image class is categorically encoded with the values randing from 0 to 9. The classes are:

Label Description
0 airplane
1 automobile
2 bird
3 cat
4 deer
5 dog
6 frog
7 horse
8 ship
9 truck

Models involved

  • The classification is done using ANN.
  • The same classification is done using CNN as well.
  • The results gathered by using an Artificial Neural Network is as below.
  • Details of the neural network
    • Activation Functions: Sigmoid, ReLU
    • Loss: Categorical Cross Entropy
    • Optimiser: Adam
    • Metric: Accuracy
    • Epochs: 5
category precision recall f1-score support
0 0.48 0.55 0.51 1000
1 0.63 0.55 0.59 1000
2 0.41 0.20 0.27 1000
3 0.32 0.27 0.29 1000
4 0.49 0.26 0.34 1000
5 0.37 0.42 0.40 1000
6 0.43 0.60 0.51 1000
7 0.44 0.61 0.51 1000
8 0.57 0.62 0.60 1000
9 0.51 0.56 0.54 1000
accuracy 0.47 10000
macro avg 0.47 0.47 0.45 10000
weighted avg 0.47 0.47 0.45 10000
  • The results gathered by using a Convolutional Neural Network is as below.
  • Details of the neural network
    • Activation Functions: ReLU, Softmax
    • Loss: Categorical Cross Entropy
    • Optimiser: Adam
    • Metric: Accuracy
    • Epochs: 5
category precision recall f1-score support
0 0.73 0.77 0.75 1000
1 0.84 0.78 0.81 1000
2 0.49 0.66 0.56 1000
3 0.51 0.52 0.51 1000
4 0.73 0.49 0.59 1000
5 0.61 0.53 0.57 1000
6 0.85 0.70 0.77 1000
7 0.66 0.81 0.72 1000
8 0.79 0.83 0.81 1000
9 0.76 0.80 0.78 1000
accuracy 0.69 10000
macro avg 0.70 0.69 0.69 10000
weighted avg 0.70 0.69 0.69 10000

Results

As we can see, for the same number of epochs there is a huge change in Artificial and Convolutional Neural network. The performance in the metrics like precision, recall, f1-score, etc. has increased for all the 10 categories.