{"id":18237480,"url":"https://github.com/sandhosh02/image_classification_use_ai_ml","last_synced_at":"2026-05-07T01:32:31.962Z","repository":{"id":259383263,"uuid":"877723615","full_name":"SANDHOSH02/Image_classification_use_AI_ML","owner":"SANDHOSH02","description":"This project focuses on detecting sensitive content in images using Convolutional Neural Networks (CNN) with TensorFlow and Keras. 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The primary goal is to classify images as either *sensitive* or *non-sensitive* by training the model on sample datasets.\n\n## Table of Contents\n\n- [Project Overview](#project-overview)\n- [Features](#features)\n- [Technologies Used](#technologies-used)\n- [Dataset](#dataset)\n- [Model Architecture](#model-architecture)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Results](#results)\n- [Future Enhancements](#future-enhancements)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Project Overview\n\nThis 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.\n\n## Features\n\n- Detects sensitive and non-sensitive content in images.\n- Uses CNN for high accuracy in image classification.\n- Can be trained and fine-tuned with custom datasets.\n- Easy-to-use interface for model training and prediction.\n\n## Technologies Used\n\n- **Python**: Programming language.\n- **TensorFlow**: Deep learning framework for model development.\n- **Keras**: High-level neural networks API for building and training the CNN.\n- **OpenCV**: For image processing (optional).\n- **NumPy**: To handle array operations.\n- **Matplotlib**: For visualizing data and results.\n\n## Dataset\n\nThe dataset used for this project consists of sample images categorized into two classes:\n\n1. **Sensitive**: Images containing private or sensitive content.\n2. **Non-Sensitive**: General images without sensitive information.\n\nYou can use any dataset that fits these categories. The dataset should be organized in the following directory structure:\n\n\n## Model Architecture\n\nThe model architecture consists of a CNN built using Keras with layers such as:\n\n- Convolutional Layers\n- Max Pooling Layers\n- Fully Connected Layers\n- Dropout for regularization\n\nThe final layer outputs a binary classification for sensitive and non-sensitive images.\n\n## Installation\n\nTo get started with the project, follow these steps:\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/your-repository-link\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandhosh02%2Fimage_classification_use_ai_ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsandhosh02%2Fimage_classification_use_ai_ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsandhosh02%2Fimage_classification_use_ai_ml/lists"}