Guide to Implementing a Convolutional Neural Network Using VeRi Dataset Case Study

Step-by-Step CNN Implementation and Image Analysis in Google Colab

Introduction

Convolutional neural networks (CNNs), a subclass of deep neural networks, are developed specifically for image synthesis and computer vision uses. They were inspired by the anatomical structure of the animal visual brain and revolutionized the field of computer vision. The many operations that CNNs can perform on the input picture include convolution, pooling, and stimulation. The central component of CNNs, the convolutional layer, uses a set of algorithms or kernels to convert the input data into a feature map. During the training process, the filters that catch different aspects of the input image, such as edges, shapes, and textures, are obtained.

The complexity of the feature images is decreased by the pooling layer's oversampling. By preventing overfitting, this helps to reduce the computational complexity of the network. In a variety of computer vision tasks, such as image processing, image recognition, and scene comprehension, CNNs have produced state-of-the-art outcomes. The network is able to recognize the complex structure of the incoming picture thanks to the activation layer's nonlinearity. Numerous uses, including autonomous transportation, medical imaging, and facial recognition, have been made use of them. CNNs are a helpful tool for computer vision and image analysis jobs. They have delivered excellent results in many real-world applications and are built to automatically acquire feature depiction of the input image.

Steps to Implement

Loading the Dataset

Load the VeRi dataset using a data loader provided by PyTorch or write your data loader. The dataset contains over 50,000 images of 776 vehicles captured by 20 cameras covering a 1.0 km2 area in 24 hours.

Figure 1: Defining Transforms and connecting to drive

At first, the drive has been mounted to the Google Colab software platform. The drive needs to be imported for accessing the datasets which contain the main dataset along with the training and the testing dataset. The command drive. mount is used for mounting the drive to google colab.

Figure 2: Loading the dataset

The datasets are then loaded into the Google Colab Platform so that the Convolution neural network can be formed.

Data pre-processing

Pre-process the dataset by resizing the images to a common size, normalizing the pixel values, and splitting the dataset into training, validation, and test sets.

Figure 3: Data Pre-processing

The data has been preprocessed. The data has been trained and tested and split according to a split ratio. The target size has been chosen as 64 for both the test and the training set.
Figure 4: Analysis of the image

The image analysis has been done using the “Goggle Colab Platform”. The image has been analyzed using the “image query” dataset that has been provided. The negative format of the image has been obtained for the analysis using “Python Programming Language”.

Data Augmentation

Figure 5: Data Augmentation Techniques

The data generation process is completed with the help of the training process. The process was even completed with the help of the image generator. That has a rescale value of 1./255, a shear range of 0.2, with a zoom range =0.2, the horizontal flip is even included here.

The data generation is even tested to complete the function or the program [1]. Its resale value is 1./255. this figure shows the Augmentation process that helps to run the function well with fewer errors.

Define the CNN architecture

The different packages have been imported for the successful implementation of the Convolution Neural Network architecture [7]. Different Convolution models have been added for the execution of the convolution neural network. The models have been compiled to get the best results.

Exploration of hyperparameters

The figure shows the expiration of hyperparameters of the function or the variables. It is trained to evaluate the loss of function. It shows the outcomes of the value loss and the value accuracy of the function, which is very important to complete the function.

Evaluation

Evaluate the performance of the CNN on the test set by computing various metrics such as accuracy, precision, recall, and F1 score.

This figure even shows twenty epochs that are very necessary to complete the dataset functions. In this data set the loss of the training dataset and accuracy of the training dataset has been obtained along with its validation loss even the validation accuracy parts are shown here. All of this even display the distinct value in the figure.

This plot or the graph is created by the data st which is trained and even evaluates the validation loss of the function.

From the data set the graph or the plot is formed with the training of data and the validation of the accuracy of the function.

The plot is created with the help of the data set. It represents the value loss and the training loss of the function or the variables. The blue line indicates the train loss whereas the yellow line indicates the value loss of the function or the variables.

Scientific Method

The plot is even created or formed with the help of the data set. it has two coloring lines one is blue and the other yellow.

the yellow line indicates the value loss of the function whereas the blue line represents the training loss of the function.

The results thus obtained are obtained using the train and the test dataset that have been provided in the image format [3]. The images are uploaded and then tested and trained for the results of the convolution neural network. The model obtained for the convolution neural network has been fitted to get desired results.

The accuracy of the model has been obtained. The accuracy and the losses have been obtained for the training dataset [5]. The total number of epochs that are taken is twenty epochs. Twenty results have been obtained for each epoch.

The plots for the losses and accuracies have been obtained. The losses for the training and validation have been plotted and the accuracies of the training and validation data have been plotted in two different graphs [9]. The loss and accuracy are proof that the model is fitted and is suitable for the neural network.

Reference list

Journal

[1] He, B., Li, J., Zhao, Y. and Tian, Y., 2019. Part-regularized near-duplicate vehicle re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3997-4005).

[2] Zhu, J., Zeng, H., Huang, J., Liao, S., Lei, Z., Cai, C. and Zheng, L., 2019. Vehicle re-identification using quadruple directional deep learning features. IEEE Transactions on Intelligent Transportation Systems, 21(1), pp.410-420.

[3] Moral, P., Garcia-Martin, A. and Martinez, J.M., 2020. Vehicle re-identification in multi-camera scenarios based on ensembling deep learning features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 604-605).

[4] Khan, S.D. and Ullah, H., 2019. A survey of advances in vision-based vehicle re-identification. Computer Vision and Image Understanding, 182, pp.50-63.

[5] Wang, H., Hou, J. and Chen, N., 2019. A survey of vehicle re-identification based on deep learning. IEEE Access, 7, pp.172443-172469

[6] He, S., Luo, H., Chen, W., Zhang, M., Zhang, Y., Wang, F., Li, H. and Jiang, W., 2020. Multi-domain learning and identity mining for vehicle re-identification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 582-583).

[7] Verma, U. and Pai, R.M., 2022. Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning. arXiv preprint arXiv:2208.06579.

[8] Qian, J., Jiang, W., Luo, H. and Yu, H., 2020. Stripe-based and attribute-aware network: A two-branch deep model for vehicle re-identification. Measurement Science and Technology, 31(9), p.095401.

[9] Luo, H., Chen, W., Xu, X., Gu, J., Zhang, Y., Liu, C., Jiang, Y., He, S., Wang, F. and Li, H., 2021. An empirical study of vehicle re-identification on the AI City Challenge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4095-4102).