hiteshydv001 / mask-detection-codeclause

Mask detection using machine learning

Mask Detection Using Machine Learning

This project is a Mask Detection system implemented using machine learning and computer vision techniques to identify whether a person in an image or video is wearing a mask (protective face covering) or not. The system contributes to safety and compliance monitoring, especially during situations that require mask-wearing.

Introduction

Mask detection is a critical application in situations that require adherence to mask-wearing guidelines, such as during a pandemic. This project demonstrates how to build, train, and evaluate a mask detection system using machine learning and computer vision.

Dataset:-

The mask detection system was trained on a labeled dataset of images and videos containing people with and without masks. The dataset includes diverse scenarios, face orientations, lighting conditions, and mask types to ensure robustness.

Roadmap:-

Mask Detection Using Machine Learning: Major Steps

1. Data Collection:
Gather a labeled dataset of images or videos containing people with and without masks. Ensure the dataset is diverse, includes various face orientations, lighting conditions, and mask types.

2. Data Preprocessing: Resize images to a consistent size to ensure compatibility with machine learning models. Normalize pixel values (typically between 0 and 1). Augment the data with techniques like rotation, scaling, and flipping to increase the dataset's size and robustness.

3. Data Labeling: Ensure that each image or video frame is labeled as "with mask" or "without mask." Annotate the dataset accurately and consistently.

4. Data Splitting: Divide the dataset into training, validation, and test sets (e.g., 70% for training, 15% for validation, 15% for testing).

5. Model Selection:

• Convolutional Neural Networks (CNNs)
• Transfer Learning (e.g., using pre-trained models like ResNet or MobileNet)
• Support Vector Machines (SVMs)
• Random Forests

6. Model Training: Train the selected model on the training dataset. Fine-tune hyperparameters, such as learning rate, batch size, and network architecture. Implement techniques like early stopping and model checkpointing to prevent overfitting.

7. Model Evaluation: Evaluate the trained model on the validation and test datasets using metrics like accuracy, precision, recall, F1-score, and ROC AUC. Check for false positives and false negatives, as they have different implications in mask detection.

Screenshots

Run Locally

Clone the project

  git clone https://github.com/Hiteshydv001/Mask-detection-codeclause.git

Go to the project directory

  cd my-project

Install dependencies

Tech Stack

Client: Anaconda || Jupyter Notebook

Server:

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