Brand Logo Detection using CNN

This project implements a Convolutional Neural Network (CNN) for detecting car brand logos. The model is trained using the car-brand-logos dataset from Kaggle and is built with Python and TensorFlow.

Table of Contents

• Introduction
• Dataset
• Model Architecture
• Usage

Introduction

Brand Logo Detection using CNN aims to accurately identify car brand logos from images. This repository includes all the necessary code to train, evaluate, and use the model.

Dataset

The dataset used for training and testing the model is the car-brand-logos dataset from Kaggle. It contains images of various car brand logos.

Model Architecture

The Convolutional Neural Network (CNN) is built using TensorFlow. The architecture includes multiple convolutional layers, pooling layers, and fully connected layers to perform the classification task.

Usage

Open the main.ipynb notebook using Kaggle (preferrably) or Jupyter Notebook.

Ensure the dataset is downloaded and structured properly before running the notebook.

Follow the instructions in the notebook to train, evaluate, and make predictions with the model.