Face Detection Using RetinaNet

The Face Detection model is trained to detect faces in images and draw bounding boxes around them by utalizing retina-net model.

Note

I ran the code on Google Colab and forgot to create the requirements.txt file. Please install the necessary dependencies manually.

Dataset

The dataset used for training and testing is the face detection dataset from DataTurks, which contains images with labeled faces.

Download and Extraction

The dataset is downloaded and extracted using the following commands in the notebook:

!wget -nc https://lazyprogrammer.me/course_files/face_detection.json

Dataset Structure

The dataset contains images with annotations for face bounding boxes. Each annotation includes the coordinates of the bounding box and the class label (face).

Example

{
  "annotation": [
    {
      "imageHeight": 333,
      "imageWidth": 650,
      "label": ["Face"],
      "notes": "",
      "points": [
        {
          "x": 0.08615384615384615,
          "y": 0.3063063063063063
        },
        {
          "x": 0.1723076923076923,
          "y": 0.45345345345345345
        }
      ]
    },
    {
      "imageHeight": 333,
      "imageWidth": 650,
      "label": ["Face"],
      "notes": "",
      "points": [
        {
          "x": 0.583076923076923,
          "y": 0.2912912912912913
        },
        {
          "x": 0.6584615384615384,
          "y": 0.46846846846846846
        }
      ]
    }
  ],
  "content": "http://com.dataturks.a96-i23.open.s3.amazonaws.com/2c9fafb064277d86016431e33e4e003d/8186c3d1-e9d4-4550-8ec1-a062a7628787___0-26.jpg.jpeg",
  "extras": null
}

Experiment Result

Training

• Epoch: 15
• Regression Loss: 1.0376
• Classification Loss: 19%

Predication Example

Prerequisites

• Python 3.x
• Required libraries: numpy, keras, tensorflow, scipy, matplotlib, PIL, requests

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

Contact

For any inquiries or support, please contact Adnan AlKharfan.