Keras Image Classifier

Efficient Image Recognition with Keras: Harnessing Deep Learning for Accurate Visual Classification

Description and Aim

• This project was created as a submission to the WARG autonomy BootCamp.
• This project aims to create a Convolutional Neural Network(CNN) which trains based on data in the CIFAR-10 dataset and have high accuracy while testing on unknown images
• The CIFAR-10 dataset contains images of 10 classes (plane,car,deer,...etc.) which the network is trained on

Implementation

• The dataset was split into training data(60,000 images) and testing data(10,000 images)
• The network was trained to fit the training images and was tested with the testing images on each epoch
• The loss function used to train was the CrossEntropyLoss, which is $\sum\limits_{i}t_i \log(p_i)$ where $t_i$ is the truth label and $p_i$ is the softmax probability of the $i$ th class, for each element in the training data
• The optimizer used to train was Adam, which is a Scholastic Gradient Descent(SGD) method based on adaptive estimations
• To avoid overfitting, extra convolutional layers were added in the network

Structure of the Network

The network has the following structure (in order):

• Input Layer
• First convolutional layer (conv1)
• Second convolutional layer (conv2)
• Third convolutional layer (conv3)
• Three fully connected layers (fc1, fc2, fc3)
• Output Layer

Result

The network performed well and was able to acheive an accuracy of ~85%

Accuracy	Loss

The exact values can be seen in the output file