https://github.com/hiteshydv001/mask-detection-codeclause
Mask detection using machine learning
https://github.com/hiteshydv001/mask-detection-codeclause
Last synced: about 1 month ago
JSON representation
Mask detection using machine learning
- Host: GitHub
- URL: https://github.com/hiteshydv001/mask-detection-codeclause
- Owner: Hiteshydv001
- Created: 2023-09-27T21:19:46.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-03T06:53:14.000Z (over 1 year ago)
- Last Synced: 2025-02-13T03:44:21.232Z (3 months ago)
- Language: Jupyter Notebook
- Size: 2.57 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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
.png?raw=true)
## Run Locally
Clone the project
```bash
git clone https://github.com/Hiteshydv001/Mask-detection-codeclause.git
```
Go to the project directory
```bash
cd my-project
```
Install dependencies
## Tech Stack
**Client:** Anaconda || Jupyter Notebook
**Server:**
## 🔗 Links
[](https://www.linkedin.com/)