A simple rest api serving a deep learning model that classifies human gender based on their faces. (vgg16 transfare learning)

Overview

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
  1. 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.

model name model description test accuracy validation accuracy train accuracy test loss validation loss train loss
gender-classification classification of gender using (vgg16 and python flask) 95.04% 91.59% 91.59% 0.1273 0.2593 0.2593

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

# precision recall f1-score support
accuracy 100% 512
macro avg 100% 100% 100% 512
weighted avg 100% 100% 100% 512

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
res.json()) .then((data) => console.log(data)); ">
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
Owner
crispengari
ai || software development. (creating brains using artificial neural nets to make softwares that has human mind.)
crispengari
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