Gender Classification

This is a simple REST api that is served to classify gender on an image given based on faces.

Starting the server

To run this server and make prediction on your own images follow the following steps

1. create a virtual environment and activate it
2. run the following command to install packages

pip install -r requirements.txt

3. navigate to the app.py file and run

python app.py

Model Metrics

The following table shows all the metrics summary we get after training the model for few 6 epochs.

Classification report

This classification report is based on the first batch of the validation dataset i used which consist of 32 images.

precision recall f1-score support

Confusion matrix

The following image represents a confusion matrix for the first batch in the validation set which contains 32 images:

Gender classification

If you hit the server at http://localhost:3001/api/gender you will be able to get the following expected response that is if the request method is POST and you provide the file expected by the server.

Expected Response

The expected response at http://localhost:3001/api/gender with a file image of the right format will yield the following json response to the client.

{
  "predictions": {
    "class": "male",
    "label": 1,
    "meta": {
      "description": "classifying gender based on the face of a human being, (vgg16).",
      "language": "python",
      "library": "tensforflow: v2.*",
      "main": "computer vision (cv)",
      "programmer": "@crispengari"
    },
    "predictions": [
      {
        "class": "female",
        "label": 0,
        "probability": 0.019999999552965164
      },
      {
        "class": "male",
        "label": 1,
        "probability": 0.9800000190734863
      }
    ],
    "probability": 0.9800000190734863
  },
  "success": true
}

Using curl

Make sure that you have the image named female.jpg in the current folder that you are running your cmd otherwise you have to provide an absolute or relative path to the image.

To make a curl POST request at http://localhost:3001/api/gender with the file female.jpg we run the following command.

curl -X POST -F [email protected] http://127.0.0.1:3001/api/gender

Using Postman client

To make this request with postman we do it as follows:

1. Change the request method to POST
2. Click on form-data
3. Select type to be file on the KEY attribute
4. For the KEY type image and select the image you want to predict under value
5. Click send

If everything went well you will get the following response depending on the face you have selected:

{
  "predictions": {
    "class": "male",
    "label": 1,
    "meta": {
      "description": "classifying gender based on the face of a human being, (vgg16).",
      "language": "python",
      "library": "tensforflow: v2.*",
      "main": "computer vision (cv)",
      "programmer": "@crispengari"
    },
    "predictions": [
      {
        "class": "female",
        "label": 0,
        "probability": 0.019999999552965164
      },
      {
        "class": "male",
        "label": 1,
        "probability": 0.9800000190734863
      }
    ],
    "probability": 0.9800000190734863
  },
  "success": true
}

Using JavaScript fetch api.

1. First you need to get the input from html
2. Create a formData object
3. make a POST requests

const input = document.getElementById("input").files[0];
let formData = new FormData();
formData.append("image", input);
fetch("http://localhost:3001/predict", {
  method: "POST",
  body: formData,
})
  .then((res) => res.json())
  .then((data) => console.log(data));

If everything went well you will be able to get expected response.

{
  "predictions": {
    "class": "male",
    "label": 1,
    "meta": {
      "description": "classifying gender based on the face of a human being, (vgg16).",
      "language": "python",
      "library": "tensforflow: v2.*",
      "main": "computer vision (cv)",
      "programmer": "@crispengari"
    },
    "predictions": [
      {
        "class": "female",
        "label": 0,
        "probability": 0.019999999552965164
      },
      {
        "class": "male",
        "label": 1,
        "probability": 0.9800000190734863
      }
    ],
    "probability": 0.9800000190734863
  },
  "success": true
}

Notebooks

The ipynb notebook that i used for training the model and saving an .h5 file was can be found:

1. Model Training And Saving