Traffic-Sign-Classification-using-deep-learning

Introduction

This project demonstrates the classification of traffic signs using deep learning techniques. Traffic sign recognition is a crucial component of autonomous vehicles, enabling them to understand and adhere to road rules.

Data

Description:

The dataset consists of images of various traffic signs, each labeled with its corresponding class. The specific details of the dataset, such as the number of classes, image resolution, and distribution, should be provided here.

Model Architecture

Deep Neural Network:

Describe the architecture of the deep neural network used for traffic sign classification.

• Input Layer: Dimensions and preprocessing steps (e.g., resizing, normalization).
• Hidden Layers: Number of layers, activation functions, and dropout rates.
• Output Layer: Number of neurons (equal to the number of classes) and activation function (e.g., softmax).

Training:

Explain the training process:

• Optimizer: Algorithm used to update model weights (e.g., Adam, SGD).
• Loss Function: Metric used to measure the model's performance (e.g., categorical cross-entropy).
• Training Parameters: Epochs, batch size, learning rate.

Evaluation

Metrics:

Describe the metrics used to evaluate the model's performance:

• Accuracy: Overall correct classifications.
• Precision: Ratio of correctly classified instances to total predicted instances.
• Recall: Ratio of correctly classified instances to total actual instances.
• F1-score: Harmonic mean of precision and recall.

Results:

Present the evaluation results, including accuracy, precision, recall, and F1-score. Visualize the results using confusion matrices or other relevant plots.