https://github.com/sandhosh02/image_classification_use_ai_ml
This project focuses on detecting sensitive content in images using Convolutional Neural Networks (CNN) with TensorFlow and Keras. The primary goal is to classify images as either sensitive or non-sensitive by training the model on sample datasets.
https://github.com/sandhosh02/image_classification_use_ai_ml
cnn-keras python tensorflow
Last synced: about 2 months ago
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This project focuses on detecting sensitive content in images using Convolutional Neural Networks (CNN) with TensorFlow and Keras. The primary goal is to classify images as either sensitive or non-sensitive by training the model on sample datasets.
- Host: GitHub
- URL: https://github.com/sandhosh02/image_classification_use_ai_ml
- Owner: SANDHOSH02
- Created: 2024-10-24T06:14:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-24T14:14:02.000Z (over 1 year ago)
- Last Synced: 2025-04-08T15:48:51.143Z (about 1 year ago)
- Topics: cnn-keras, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 79.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Image Classification using AI/ML to Predict Sensitive and Non-Sensitive Data
This project focuses on detecting sensitive content in images using Convolutional Neural Networks (CNN) with TensorFlow and Keras. The primary goal is to classify images as either *sensitive* or *non-sensitive* by training the model on sample datasets.
## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Technologies Used](#technologies-used)
- [Dataset](#dataset)
- [Model Architecture](#model-architecture)
- [Installation](#installation)
- [Usage](#usage)
- [Results](#results)
- [Future Enhancements](#future-enhancements)
- [Contributing](#contributing)
- [License](#license)
## Project Overview
This project implements a machine learning model to classify images into sensitive or non-sensitive categories. It uses a Convolutional Neural Network (CNN) built with TensorFlow and Keras to make predictions after being trained on a dataset containing labeled sensitive and non-sensitive images.
## Features
- Detects sensitive and non-sensitive content in images.
- Uses CNN for high accuracy in image classification.
- Can be trained and fine-tuned with custom datasets.
- Easy-to-use interface for model training and prediction.
## Technologies Used
- **Python**: Programming language.
- **TensorFlow**: Deep learning framework for model development.
- **Keras**: High-level neural networks API for building and training the CNN.
- **OpenCV**: For image processing (optional).
- **NumPy**: To handle array operations.
- **Matplotlib**: For visualizing data and results.
## Dataset
The dataset used for this project consists of sample images categorized into two classes:
1. **Sensitive**: Images containing private or sensitive content.
2. **Non-Sensitive**: General images without sensitive information.
You can use any dataset that fits these categories. The dataset should be organized in the following directory structure:
## Model Architecture
The model architecture consists of a CNN built using Keras with layers such as:
- Convolutional Layers
- Max Pooling Layers
- Fully Connected Layers
- Dropout for regularization
The final layer outputs a binary classification for sensitive and non-sensitive images.
## Installation
To get started with the project, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/your-repository-link