https://github.com/myatmyintzuthin/face_mask_detection

Training a face mask detector using TensorFlow2 object detection API.
https://github.com/myatmyintzuthin/face_mask_detection

deep-learning face-mask-detection ssd-mobilenetv2 tensorflow-object-detection-api

Last synced: 16 days ago
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Training a face mask detector using TensorFlow2 object detection API.

• Host: GitHub
• URL: https://github.com/myatmyintzuthin/face_mask_detection
• Owner: myatmyintzuthin
• License: mit
• Created: 2021-12-19T06:00:47.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-12-19T07:39:48.000Z (about 3 years ago)
• Last Synced: 2023-08-02T10:39:38.686Z (over 1 year ago)
• Topics: deep-learning, face-mask-detection, ssd-mobilenetv2, tensorflow-object-detection-api
• Language: Jupyter Notebook
• Homepage:
• Size: 6.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# face_mask_detection

[![TensorFlow 2.6](https://img.shields.io/badge/TensorFlow-2.6-FF6F00?logo=tensorflow)](https://github.com/tensorflow/tensorflow/releases/tag/v2.6.0) 

![webcam_test](https://github.com/myatmyintzuthin/face_mask_detection/blob/main/assets/webcam_test.gif)

------

#### Dataset

The [Face mask Dataset](https://github.com/myatmyintzuthin/face_mask_detection/releases/download/v1.0.0/face_mask_dataset.zip) is collected and annotated by Self-Study-Camp team members on 22 November 2021. The dataset consists of 208 images splitted into 168 train, 40 test images in XML format.

![dataset.PNG](https://github.com/myatmyintzuthin/face_mask_detection/blob/main/assets/dataset.PNG)

------

#### Model 

We will be using SSD-mobilenetv2 model from [TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md). They provide a collection of detection models pre-trained on the [COCO 2017 dataset](https://cocodataset.org/).

| Model name  | Speed (ms)  | COCO mAP | Outputs |

| ----------- | ----------- | -------- | ------- |

| [SSD MobileNet V2 FPNLite 640x640](http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz)      | 39      | 28.2 | Boxes |

------

#### Metrics

After training for 8000 steps:

```

{'Loss/classification_loss': 0.06144937,

 'Loss/localization_loss': 0.017765515,

 'Loss/regularization_loss': 0.12342198,

 'Loss/total_loss': 0.20263687,

 'learning_rate': 0.07603875}

```

Validation Detection Metrics:

```

Accumulating evaluation results...

DONE (t=0.06s).

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.343

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.698

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.399

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.515

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.313

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.348

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.449

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.552

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.700

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.693

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.511

```

#### Tensor Board Metrics

![tensorboard.png](https://github.com/myatmyintzuthin/face_mask_detection/blob/main/assets/tensorboard.png)

------

#### References

[TensorFlow Object Detection API Tutorial](https://readthedocs.org/projects/tensorflow-object-detection-api-tutorial/) \

[TensorFlow 2 Detection Model Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md)