Master Traffic Image Classification with Abina's AI Model

Table of Contents

1. Introduction
2. Model Functionality
3. Running the Application
4. Loading Data
5. Converting and Resizing Images
6. Labels and Evidence
7. Get Model Function
8. Training and Accuracy
9. Evaluating the Model
10. Conclusion

Introduction

Abina introduces herself and explains that she will be demonstrating the functionality of her model for the CS50's Introduction to Artificial Intelligence project on traffic. She mentions that she will show the application in action and provide a step-by-step explanation of the code.

Model Functionality

Abina describes the functionality of her model, which includes convolutional layers preceded by max pooling layers, flattened Hidden dense layers, and an output layer. She mentions that the model is designed to classify images into various categories based on specified assignments.

Running the Application

Abina demonstrates how to run the code and mentions that it may take some time for the application to load the data. She explains that the code reads images using the OpenCV library and converts them into numpy arrays. The images are then resized and appended to a list along with their corresponding labels.

Loading Data

Abina explains the process of loading the data. She mentions that the code goes through each category and lists all the files in the folder, regardless of the user interface being used. The list of files is then converted into evidence, which consists of images and labels.

Converting and Resizing Images

Abina describes the steps involved in converting and resizing the images. She explains that the code uses the imread function of the OpenCV module to read each file. The images are converted into numpy arrays and resized to a specified Height.

Labels and Evidence

Abina discusses the importance of ensuring that the length of the list of evidence is the same as that of labels. She explains that the evidence, which includes images and labels, is appended to the labels list. Abina emphasizes the necessity of this step as a precaution.

Get Model Function

Abina provides an overview of the get_model function. She explains that the function consists of convolutional layers preceded by max pooling layers and is followed by flattened hidden dense layers. The function also includes an output layer for images of non-categories.

Training and Accuracy

Abina demonstrates the training process of the model. She shows the progress of each training approach and mentions that the accuracy improves with each approach. Abina notes that the training time may vary depending on whether or not screen casting is used.

Evaluating the Model

Abina discusses the evaluation process of the model. She reveals that the evaluated accuracy is 99.51% and expresses satisfaction with the performance.

Conclusion

Abina concludes her demonstration by stating that her model has completed training and has achieved an accuracy of 99.51%. She expresses her contentment with the results and expresses gratitude for the opportunity to Present her code.

🚦 Model Functionality

Abina's model demonstrates the functionality of an artificial intelligence system designed to classify images related to traffic. The model utilizes convolutional layers, max pooling layers, and hidden dense layers to process and classify the images. With an output layer specifically designed for images of non-categories, the model aims to achieve high accuracy in classification.

🏃 Running the Application

To run the application, navigate to the gdsrb folder and execute the code. Please note that it may take some time for the application to load the data. Once the application starts, you will observe the progress of the training process, which will display the accuracy of the model with each training approach.

📂 Loading Data

The first step in the application is to load the data. The code iterates through each category and lists all the files in the folder, regardless of the user interface being used. These files are then converted into evidence, which includes both images and their respective labels.

🖼️ Converting and Resizing Images

Next, the code converts the images using the OpenCV library. The imread function reads each file, and the images are then converted into numpy arrays. After conversion, the images are resized to a specified height.

📝 Labels and Evidence

To ensure proper alignment, the code confirms that the length of the list of evidence matches that of the labels. The evidence, consisting of images and labels, is then appended to the labels list. This step is important for accurate classification.

🔍 Get Model Function

The get_model function is responsible for establishing the structure of the model. It consists of convolutional layers followed by max pooling layers. The model also includes flattened hidden dense layers and an output layer for images of non-categories. The input Shape, specified in the function, is based on the image's energy height and channels.

🎓 Training and Accuracy

The model undergoes a series of training approaches to improve its accuracy. The accuracy of the model is displayed after each training approach, indicating the progress being made. It is worth noting that the training time may vary depending on the use of screen casting.

📊 Evaluating the Model

Once the training is complete, the model is evaluated to determine its accuracy. Abina's model achieved an accuracy of 99.51%, which indicates a high level of performance in accurately classifying traffic-related images.

💡 Conclusion

Abina's demonstration showcases the functionality and effectiveness of her model. With a high accuracy rate of 99.51%, her model exhibits strong potential for image classification in the field of traffic. The thorough explanation and step-by-step process provide valuable insights into the inner workings of the model.

FAQ

Q: How long does it take to train the model? A: The training time may vary depending on whether screen casting is used. In the absence of screen casting, the training process takes approximately 26 seconds. However, when screen casting is enabled, the training time increases to around 56 seconds.

Q: What is the evaluated accuracy of the model? A: The evaluated accuracy of Abina's model is 99.51%, demonstrating its high level of accuracy in classifying traffic-related images.