https://github.com/ansh420/traffic-sign-classification-using-deep-learning

we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.
https://github.com/ansh420/traffic-sign-classification-using-deep-learning

classification deep-learning traffic-sign-classification traffic-sign-recognition

Last synced: 7 months ago
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we will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.

• Host: GitHub
• URL: https://github.com/ansh420/traffic-sign-classification-using-deep-learning
• Owner: Ansh420
• Created: 2023-02-16T03:21:32.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2025-02-28T07:53:16.000Z (7 months ago)
• Last Synced: 2025-02-28T14:42:19.572Z (7 months ago)
• Topics: classification, deep-learning, traffic-sign-classification, traffic-sign-recognition
• Language: Jupyter Notebook
• Homepage:
• Size: 21.5 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
![5501de592aba](https://github.com/user-attachments/assets/8fce5a51-7062-422c-8ff5-caf2bc4ad0e6)

# 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.