DarkNeurons: An Automated Deep Learning Library

Hello to all AI Enthusiasts out there, Hope you are doing good in this pandemic era and improving your skills.

DarkNeurons is an Open-Source implementation of Automatic Deep Learning Library which can reduce the time and Complexity for non-technical users to train their own networks without Compromising Accuracies for Classification of Images and Object Detection, the most demanding techniques for Autonomous Systems and Medical Fields.

" By augmenting human performance, AI has the potential to markedly improve productivity, efficiency, workflow, accuracy and speed, both for physicians and for patients … What I’m most excited about is using the future to bring back the past: to restore the care in healthcare. " - Eric Topol

CONTENTS:

• Installation

• Classification

• Object Detection

INSTALLATION:

Two ways to install Library:

$ pip install DarkNeurons

or

git clone https://github.com/Tushar-ml/DarkNeuron.git

cd DarkNeuron

python setup.py install

Providing the Summary of Methods in DarkNeurons below, Detailed Instructions can be seen over here: Show Your Support by Forking the Repository and by Providing contribution to it.

https://github.com/Tushar-ml/DarkNeuron

CLASSIFICATION:

DarkNeuron Classification has the feature of implementing Pretrained Models on ImageNet Data. Users can directly train pre-trained models or can retrain their own models. Models provided are:

• InceptionV3

• Xception

• ResNet50

• VGG16

• VGG19 Further will be added on upcoming releases.

• Initialization:

from DarkNeurons import Classify_Images

classify = Classify_Images( working directory )

• Preparation and Preprocessing of Data:

  Train , Valid , Test = classify.Preprocess_the_Image( )

• Model Creation

  model = classify.Create_the_Model( )

• Model Training

  model = classify.Train_the_Model( )

• Prediction

  classify.Predict_from_the_Model( )

• Visualization of Metrics and Prediction

  classify.Visualize_the_Metrics( )

  classify.Visualize_the_Predictions( )

OBJECT DETECTION

Object Detection Model uses the YOLOv4 version for Detection. It is the fastest algorithm known for Detection

• Initialization:

  from DarkNeurons import YOLOv4

  yolo = YOLOv4( working directory )

• Preparation of Data:

  yolo.Prepare_the_Data( )

• Model Training

  yolo.Train_the_Yolo( )

• Detection

  yolo.Detect( )

In the Detection part, you can detect from Image, Video or even Live Capturing from Screen Detection.

Further Releases:

• Visualization of Artificial Neural Networks

• Hybridization or Fusion of Two Different Models

• Object Tracking using DeepSORT

Improve your Deep Learning Skills by Contributing to the Open-Source Libraries.

Keep Coding, Keep Growing with AI

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Thank You!!!!