Real-Time_Face_Mask_Detection_with_YOLOv10

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Real-Time_Face_Mask_Detection_with_YOLOv10

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This repository contains a complete workflow for training and deploying a state-of-the-art YOLOv10 model for real-time face mask detection. The model accurately identifies 'with_mask' and 'unmasked' individuals in images and videos. Includes Google Colab notebooks for training and inference

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# Real-Time Face Mask Detection with YOLOv10

![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white)
![YOLO](https://img.shields.io/badge/YOLOv10-blue?style=for-the-badge&logo=yolo&logoColor=white)
![Google Colab](https://img.shields.io/badge/Colab-F9AB00?style=for-the-badge&logo=googlecolab&color=525252)

A high-performance, real-time system for detecting faces with and without masks, leveraging the state-of-the-art **YOLOv10** object detection model. This project is fine-tuned on a custom dataset to provide accurate and efficient monitoring in various environments.

<p align="center">
  <img src="https://raw.githubusercontent.com/Muntasirzx/EEE343/fe3afca8b1b5c8440c309d1cf45848613d9d239f/Data/mask_unmasked_1-ezgif.com-optimize.gif" alt="Project Banner"/>
</p>
---

## 🚀 Project Overview

The goal of this project is to accurately identify and differentiate between individuals wearing masks (`with_mask`) and those who are not (`unmasked`), using video or image data. By fine-tuning the incredibly efficient YOLOv10 model, this system achieves high accuracy and real-time processing speeds, making it suitable for deployment in public spaces, workplaces, and other areas where health and safety monitoring is crucial.

### ✨ Key Features
- **State-of-the-Art Model**: Utilizes **YOLOv10n**, the latest in the YOLO series, known for its exceptional speed and accuracy.
- **Custom-Trained**: Fine-tuned on a custom dataset specifically for mask detection, ensuring high performance on the target classes.
- **Two-Class Detection**: Accurately classifies two distinct categories: `with_mask` and `unmasked`.
- **High Performance**: Achieves an impressive **mAP(50-95) of 0.856**, demonstrating robust detection capabilities.
- **Reproducible Workflow**: Includes pre-configured Google Colab notebooks for both training and inference, allowing for easy replication and use.

---

## 🔬 In-Depth Dataset Analysis

A thorough analysis of the dataset was conducted to understand its characteristics before training.

### Class and Bounding Box Distribution
![Label Distribution and Bounding Box Analysis](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/labels.jpg)
* **Class Imbalance**: The bar chart clearly shows that the dataset contains significantly more instances of the `with_mask` class (approx. 1350 instances) than the `unmasked` class (approx. 450 instances). This is a realistic scenario but one that requires a robust model to prevent bias.
* **Spatial Distribution**: The scatter plot of `y` vs `x` coordinates shows that the objects (faces) are predominantly centered within the images, which is ideal for training.
* **Size Distribution**: The `height` vs `width` scatter plot indicates a consistent aspect ratio for the bounding boxes, suggesting that the objects were captured from a similar distance and perspective.

### Bounding Box Attribute Correlation
![Label Correlogram](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/labels_correlogram.jpg)
* The correlogram provides a deeper look into the relationships between bounding box attributes. The strong correlations between `x/y` and `width/height` confirm the centralized and uniformly-sized nature of the objects in the dataset, which contributes to stable training.

---

## 📊 Model Performance Deep Dive

The model was trained for 50 epochs, and its performance was rigorously evaluated.

### Overall Performance Metrics

The final metrics demonstrate a highly effective model, especially for the majority class.

| Class      | Images | Labels | Precision | Recall | mAP50-95 | mAP50    |
|------------|--------|--------|-----------|--------|----------|----------|
| `with_mask`| 548    | 1339   | 0.932     | 0.898  | 0.923    | 0.985    |
| `unmasked` | 548    | 434    | 0.893     | 0.818  | 0.789    | 0.949    |
| **all** | **548**| **1773**| **0.913** | **0.858**| **0.856**| **0.967**|

### Performance Curves Explained

#### Precision-Recall Curve
![PR Curve](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/PR_curve.png)
* This curve shows the trade-off between precision (how many detections are correct) and recall (how many actual objects are found). The large area under the curve, resulting in a mean Average Precision (mAP) of **0.856**, indicates that the model maintains high precision even while achieving high recall.

#### F1-Confidence Curve
![F1 Curve](https://raw.githubusercontent.com/Muntasirzx/EEE343/8655bfb45fbd8ab9b8093307d0c725a6b60c7217/Data/F1_curve.png)
* The F1-Score represents the harmonic mean of precision and recall. This curve is crucial for selecting the optimal confidence threshold for deployment. Our model achieves its peak F1-Score of **0.86** at a confidence threshold of **0.420**, providing the best balance between not missing objects and not making false detections.

#### Recall-Confidence Curve
![Recall Curve](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/R_curve.png)
* This curve illustrates how recall is affected as we change the confidence threshold. It shows that we can still achieve over 80% recall even at a high confidence of ~0.7, demonstrating the model's reliability.

### Confusion Matrix Analysis
![Normalized Confusion Matrix](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/confusion_matrix.png)
* The confusion matrix gives a clear, normalized view of the model's classification performance.
    * **`with_mask` class**: Correctly identified **90%** of the time.
    * **`unmasked` class**: Correctly identified **83%** of the time.
    * **Primary Error**: The model's main confusion lies in misidentifying the background as an `unmasked` person (15% of the time). This is a typical challenge in object detection and suggests that adding more diverse background images (negative samples) could further improve performance.

---

## ✅ Validation Showcase

The following images are predictions from the validation set, demonstrating the model's robust performance on unseen data.

![Validation Batch 0](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/val_batch0_pred.jpg)
![Validation Batch 1](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/val_batch1_pred.jpg)
![Validation Batch 2](https://raw.githubusercontent.com/Muntasirzx/EEE343/eb21b1054ea33a5c0e168d0b24445566a4d846b4/Data/val_batch2_pred.jpg)

---

## 🛠️ Getting Started

You can easily replicate the training process or run inference using your own data with the provided Google Colab notebooks.

### Prerequisites
- A Google Account (for using Google Colab and Google Drive)
- A video file or image for inference

### 1. Training

To train the model on your own custom dataset, follow the steps outlined in the training notebook:

**Notebook:** [`Mask_Detection_with_YOLOv10.ipynb`](https://github.com/Muntasirzx/EEE343/blob/545f48509604a70d6c843029b3acd29a8866ea96/Files%20and%20Codes/Mask_Detection_with_YOLO10.ipynb)

1.  **Set up Your Dataset**: Organize your images and YOLO-formatted `.txt` label files into `train/`, `valid/`, and `test/` directories.
2.  **Configure `data.yaml`**: Create a YAML file that points to your dataset directories and defines your class names and number.
3.  **Upload to Google Drive**: Place your dataset and YAML file in your Google Drive.
4.  **Run the Notebook**: Open the notebook in Google Colab, update the file paths, and run the cells sequentially to start training. The final weights (`best.pt`) will be saved in your Google Drive.

### 2. Inference

To run inference on a new video or image using the pre-trained weights:

**Notebook:** [`Inference_Mask_detection.ipynb`](https://github.com/Muntasirzx/EEE343/blob/545f48509604a70d6c843029b3acd29a8866ea96/Files%20and%20Codes/Inference_Mask_detection.ipynb)

1.  **Upload Your Files**:
    * Upload your trained model weights (`best.pt`) to your Google Drive.
    * Upload the video or image you want to test to your Google Drive.
2.  **Run the Notebook**: Open the inference notebook in Google Colab.
3.  **Update Paths**: Modify the cell for prediction to point to your `best.pt` file and your source video/image.
4.  **Execute and Download**: Run the prediction cell. The resulting video with bounding boxes will be saved in the `runs/detect/predict/` directory within the Colab session. You can download it directly from the file browser.

---

## 🙏 Acknowledgements
* This project is built upon the excellent work by the **YOLOv10** authors at Tsinghua University. [YOLOv10 Paper](https://arxiv.org/abs/2405.14458)
* The training framework is powered by the **Ultralytics** library. [Ultralytics GitHub](https://github.com/ultralytics/ultralytics)

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