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A Keras deep learning image classifiers on Django server with REST API
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README.md

AI Image classifiers on Django server with REST API


python3.5 python3.6 django2.1.5 Build Status

Introduction

A Keras deep learning image classifiers on Django server with REST API. It can help you quickly deploy and apply ML models.

Description

Machine Learning (ML) models are typically trained and test on benchmark data sets. However, such data sets may not represent the data in real applications. These ML models should be updated after tested on real data sets. Deployment of the models after each update may be time consuming. Therefore, automated deployment is needed to save time and effort. In this proposal, we describe our solution to integrate trained ML models into any application of interest. We use Django web-based platform to build REST API for model deployment. Jenkins is used for continuous integration and automated deployment of ML models into Django servers.

Features

  • Add Account management funtion
  • Add Login & Signup
  • Add Django Rest Framework
  • Optimize front-end interface, adapt to mobile and PC interface
  • Design front-end and back-end interactive interfaces
  • Add feature: Face comparison
  • Add feature: Bank card identification
  • Add feature: Gesture identification
  • Add feature: Image classify using following model:
    • ResNet50
    • Xception
    • MobileNet, MobileNetV2
    • InceptionV3, InceptionResNetV2
    • DenseNet121, DenseNet169, DenseNet201
    • VGG16, VGG19
    • NASNetMobile, NASNetLarge
  • Run each part of functions seperately in local and cloud server
  • Run all the functions in local server and cloud server
  • Design and using machine learning CI/CD Tools such as Git, Jenkins, Nginx, uwgsi for deployment.

Architicture

Result01.png

Requirements

pip3 install -r requirements.txt

Usage

I assume you already have your own local virtual environment.

git clone https://github.com/nature1995/image-classify-django-server.git
pip install -r requirements.txt
python manage.py makemigrations
python manage.py migrate
python manage.py runserver 0.0.0.0:8000

Access the web page though this link: http://127.0.0.1:8000/

Name Description
Face comparison Extract and analyze facial features in pictures
Bank card identification Identify the field information above the bank card
Gesture identification Detect and return the meaning of the gesture in the picture.
Image classify Detects objects in the image, returns the detected object name, and the corresponding confidence.

Web API for image classify

Name Input Size API address
ResNet50 224x224 http://127.0.0.1:8000/predict/api/
Xception 299x299 http://127.0.0.1:8000/predict_Xception/api/
MobileNet 224x224 http://127.0.0.1:8000/predict_MobileNet/api/
MobileNetV2 224x224 http://127.0.0.1:8000/predict_MobileNetV2/api/
InceptionV3 299x299 http://127.0.0.1:8000/predict_InceptionV3/api/
InceptionResNetV2 224x224 http://127.0.0.1:8000/predict_InceptionResNetV2/api/
DenseNet121 224x224 http://127.0.0.1:8000/predict_DenseNet121/api/
DenseNet169 224x224 http://127.0.0.1:8000/predict_DenseNet169/api/
DenseNet201 224x224 http://127.0.0.1:8000/predict_DenseNet201/api/
VGG16 224x224 http://127.0.0.1:8000/predict_VGG16/api/
VGG19 224x224 http://127.0.0.1:8000/predict_VGG19/api/
NASNetLarge 331x331 http://127.0.0.1:8000/predict_NASNetLarge/api/
NASNetMobile 224x224 http://127.0.0.1:8000/predict_NASNetMobile/api/

Input

Parameter Type Description
image file Image file that you want to classify.
top text
(optional, default=6)
Return top-k categories of the results. Must me string in integer format.

Note: You can not send a very large size image.

Result

Parameter Type Description
success bool Whether classification was sucessfuly or not
predictions label, float pair of label and it's probability

Accuracy for individual models

Model Accuracy Top-6 Accuracy
Xception 0.780 0.955
VGG16 0.722 0.914
VGG19 0.723 0.910
ResNet50 0.748 0.931
InceptionV3 0.789 0.948
InceptionResNetV2 0.813 0.963
MobileNet 0.714 0.904
MobileNetV2 0.723 0.912
DenseNet121 0.761 0.934
DenseNet169 0.772 0.952
DenseNet201 0.783 0.945
NASNetMobile 0.752 0.920
NASNetLarge 0.837 0.959

Example

Using Postman to test the API:
POST http://127.0.0.1:8000/predict_VGG19/api/

Result

{
    "success": true,
    "predictions": [
        {
            "label": "red_fox",
            "probability": 0.8969062566757202
        },
        {
            "label": "kit_fox",
            "probability": 0.08841043710708618
        },
        {
            "label": "grey_fox",
            "probability": 0.012036639265716076
        },
        {
            "label": "Arctic_fox",
            "probability": 0.0022438077721744776
        },
        {
            "label": "coyote",
            "probability": 0.0002566342300269753
        },
        {
            "label": "white_wolf",
            "probability": 0.00005685776341124438
        }
    ]
}

Compatibility

The codes are tested using Travis-CI platform with django 2.1.x and Python 3.5, 3.6

Issue

If you have questions or issues, please feel free to tell us.

Contribution

Welcome to make pull request. If you have a related project/component/tool, add it with a pull request to add it!

Others

Admin Account

python manage.py createsuperuser

username: ranxiaolang
email: YOUR EMAIL  
password: ranxiaolang  

Access the web page though this link: http://127.0.0.1:8000/admin

Django Restframework

Access the web page though this link: http://127.0.0.1:8000/iot/

Author

License

This software is licensed under the MIT License. For more information, read the file LICENSE.

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