deoldify

DeOldify – Download Github, Alternatives & How to Use/Setup in a Minute

DeOldify – How to Use AI and Setup Image Colorization Process

Muhammad Imran

Author 

March 17, 2020

In this guide, I will explain how you can colourize old images and videos using deoldify and ai image colourization.

deoldify

What is Deoldify?

Colorizing images has become more than an art technique and is now a real-world application, as more people continue to add extra details in their images. More costly, hectic and time-consuming methods of colorization have now been replaced with advanced technologies such as machine learning solutions. These may not be rendered as optimal solutions, but they offer impressive results. DeOldify is one such machine learning as a service which includes pre-trained weights, allowing you to colorize black and white images or video with minimal effort!

DeOldify Download Github Repository:

DeOldify is an open-source deep learning model, created by Jason Antic, to help colorize and restore old and grayscale images. This deep learning model is responsible for adding new life to dull and dark old images and film footage. Adding details into old images has become easy with DeOldify, which uses the machine learning technology. Use pre-trained weights to add color to grayscale images or videos with the most minimal effort.

You can download repo from below links:

https://github.com/alfagao/DeOldify

AI Colorization Process

The world has always been full of color but until recently we lacked the technology to capture its beautiful colors and the pictures were mostly black and white. But these can also be colorized to achieve realistic results, using AI. This method involves training a Convolutional Neural Network (CNN) on a large number of lab colorspace photos. The colospace method corresponds better with the human visual system than RGB. The images are made up of three channels and the predictive model is trained to predict a (red-green) or b (blue-yellow) depending on the lightness channel (input). By recombining into a colorized image, it can be converted back to RGB. 

This technique can offer good results on a number of materials, such as landscapes and countryside. However, results may not be the same for more complicated scenes. Even then it is less tedious than manual techniques and offers better results.

How to Use and Setup Deoldify (Step by Step Process)

For those who are looking to try it themselves; connect to the directory you want DeOldify to be placed in, then clone it via https://github.com/jantic/DeOldify.git DeOldify.

  1. After doing this, connect to the freshly created DeOldify directory to create a new “models” directory for your pre-trained weights.
  2. Now download the pre-trained weights via the command line: wget https://www.dropbox.com/s/zkehq1uwahhbc2o/ColorizeArtistic_gen.pth?dl=0 -O ./models/ColorizeArtistic_gen.pth
  3. You can also manually download pre-trained weights in the DeOldify, in case you already do not have them.
  4. Now install dependencies:
  5. pip install -r requirements.txt
  6. Once this is done, open the project in a Jupyter Notebook. 
  7. You should now be able to open the ImageColorizer.ipynb file and colorize from there.

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Deoldify Alternatives

Pigment

It is a unique application that allows you to explore colors utilizing lighting and pigments.

 

Colorize It

enables you to colorize black and white photos using deep learning algorithms. 

 

Pikka

This is an easy to use color picker for developers and designers, working on multiple screens.

 

Grayscale the Web

This application does the reverse of colorization, as it turns websites gray by removing color. 

 

NeuralPainting

It facilitates real-time conversion of videos and images into neural art. 

 

Colorize.cc

It utilizes powerful Machine Learning models to colorize black and white photos. 

 

Deep Style

Utilizes deep learning, enabling artistic style transfer. 

 

Colorado App

Offers great color schemes, shared by their creative community.

DeOldify Reddit Reviews 

DeOldify has been discussed by numerous members on Reddit. Most of the members have shared their positive reviews and experiences with DeOldify on Reddit. Some members have also shared their work with other members to show them the quality of work they can get with DeOldify. Furthermore, members also share any updates or information they receive regarding DeOldify. One such example of this is a discussion, posted about two months ago, about a tweet shared by the creator of DeOldify, that stated that the new versions of this software will not use GANs anymore, but something else related to the concept of perceptual loss applied in super-resolution. This discussion had comments from other users too, who gave in their two cents regarding this new update.

From the Reddit reviews, it is safe to say that people generally like the DeOldify platform for editing their old and grayscale pictures. However, there are numerous other options available on the market, as mentioned above. 

deoldify reddit reviews

Free Film Colorization Software

Apart from DeOldify, there are numerous other options available on the market. A few of the other colourization software are mentioned below:

—Deep AI 

—Colourise.sg

—Algoritmia

—Pigment 

—Colorize It 

—Pikka 

—Grayscale the Web

—NeuralPainting 

—Colorize.cc

—Deep Style 

—Colorado App

FAQs:

How do you color old photos?

Colorizing black and white photos is not a new phenomenon, from being colored by hand in the past, these are being done with digital ai image processing solution or like Photoshop and other computer vision applications that produce more realistic and historically accurate results. Simple graphic tablets and a pen can also be utilized to colorize images. For this, the image has to be restored first, including the removal of stains and scratches. Then it just comes down to choosing the right tones and adding multiple layers to achieve higher accuracy and better feel. This process can take a couple of hours or even days. A good understanding of light can help produce meticulous results.

What is image colorization?

Image colorization is the process of converting a grayscale (black and white) image into a colorized image by adding colors and tones that can bring realistic accuracy.

How do you use DeOldify if you don’t have any AI experience?

Since DeOldify is a pre-trained model, which means it has been fed large amounts of input already; it can be used by anyone, regardless of their level of AI exposure.

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We are the Pioneers in the AI Image Colorization Arena  – Do you want to become a pioneer yourself ? Get In Touch

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

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advantages of neural networks

Advantages of Neural Networks – Benefits of AI and Deep Learning

Advantages of Neural Networks – Benefits of AI and Deep Learning

Muhammad Imran

Author 

March 9, 2020

advantages of neural networks

Neural networks can be known as a set of algorithms, loosely modeled after the brain of humans that are designed in a way to recognize patterns. They are capable of interpreting data using a type of machine perception, clustering or labeling raw input. The patterns recognized by them are contained in vectors, numerical, into which all the data of the real-world, be it text, sound, time series, or images, are supposed to be translated.

Folio3 has been a reliable service provider of machine learning solutions for enterprises in order to help them enhance their understanding of customer needs and perform better. Our team of experienced and determined consultants and data scientists have successfully delivered a large number of end-to-end projects that are related to natural language processing, machine learning, predictive analysis, and computer vision.

Along with various advantages of neural networks, the most common ones are that they help us classify and cluster. They can be considered as a classification of the clustering layer maintained above the data that you store and manage. They allow you to group the data that is unlabeled based on similarities between example inputs, and they are responsible for the classification of data when the dataset is labeled by them to train on. To be more precise, neural networks can be considered as components of larger applications of machine learning as a service that involve algorithms for classification, regression, and reinforcement learning.

What Are The Types of Neural Networks

There are different types of neural networks. They all use different principles and determine their own rules. There are various types of artificial neural networks and each one of them comes with a unique and special strength.

1) Feedforward Neural Network – Artificial Neuron

The simplest type of artificial neural network. In this type, data is passed through various input nodes until it finally reaches the output node.

To be better said, the movement of data is only in one direction. This is also called front propagated wave that is achieved usually by classification of the activation function. This neural network may only have one layer or many hidden layers.

In a feedforward neural network, the sum of products of all the inputs and their weights are calculated, which is later fed to the output.

2) Radial Basis Function Neural Network

This type of neural network considers the distance of any certain point relative to the center. These networks have two layers. In the inner layer, the features are paired up with the radial basis function. The output of the given features is considered when the same output gets calculated in the next time-step.

This type of neural network is usually applied in power restoration systems.

3) Multilayer Perceptron

This neural network has three or more than three layers. It is basically used for the classification of the data that cannot be linearly separated. This type of artificial neural network is fully connected and that is because each and every single node present in a layer is connected to nodes in the next layer.

It uses a nonlinear activation function. A multilayer perceptron neural network is applied in machine translation and speech recognition technologies.

4) Convolutional Neural Network

This type of neural network uses a variation of the multilayer perceptrons. Convolutional neural networks contain single or more than one layer that can be pooled or entirely interconnected.

They show good results in paraphrase detection and semantic parsing. They are applied in image classification and signal processing.

5) Recurrent Neural Network(RNN) – Long Short Term Memory

It is a type of artificial neural network where a particular layer’s output is saved and then fed back to the input. This helps in predicting the outcome of a layer. The formation of the first layers is the same as it is in the feedforward network.

The recurrent neural network starts with front propagation but it remembers all the information that is might need to use later.

The artificial neural network is used in text-to-speech conversion technology.

6) Modular Neural Network

This neural network has many different networks functioning independently, performing sub-tasks. They do not do any kind of interaction with one another during the process of computation. The independently work to achieve the output.

7) Sequence-To-Sequence Models

This contains two recurrent neural networks. An encoder is present that processes the input and the output is processed by a decoder. The encoder and decoder can use similar or even different parameters.

Sequence-To-Sequence models are mainly applied in question answering, machine translations systems, and chatbots.

What Are The Advantages of Neural Networks

There are various advantages of neural networks, some of which are discussed below:

1) Store information on the entire network

Just like it happens in traditional programming where information is stored on the network and not on a database. If a few pieces of information disappear from one place, it does not stop the whole network from functioning.

2) The ability to work with insufficient knowledge:

After the training of ANN, the output produced by the data can be incomplete or insufficient. The importance of that missing information determines the lack of performance.

3) Good falt tolerance:

The output generation is not affected by the corruption of one or more than one cell of artificial neural network. This makes the networks better at tolerating faults.

4) Distributed memory:

For an artificial neural network to become able to learn, it is necessary to outline the examples and to teach it according to the output that is desired by showing those examples to the network. The progress of the network is directly proportional to the instances that are selected.

5) Gradual Corruption:

Indeed a network experiences relative degradation and slows over time. But it does not immediately corrode the network.

6) Ability to train machine:

ANN learn from events and make decisions through commenting on similar events.

7) The ability of parallel processing:

These networks have numerical strength which makes them capable of performing more than one function at a time.

Advantages of Convolutional Neural Networks for Businesses and Enterprises

Utilizing intelligent automation in order to boost up the growth process of your business might become your best decision to stay ahead of your competitors. Let’s discuss what are some key benefits of neural networks for business and enterprises.

1) Cost and Time Benefits

Among the three most common neural networks advantages, cost and time benefit remains on the top. Neural networks are considered ad trainable brains. You feed them information about your organization and train them in order to perform tasks such as report generation. These networks will use that new information, training, and work experience to improve and adapt in a similar way that a human worker learns.

However, these networks are faster than the human workforce and function at a rapid pace and nor do they sleep. Using deep learning in your business operations can enable you to save money and time that you spend in hiring workers or in outsourcing for certain projects. You can no only save money and time but also have your work done faster and error-free.

2) Quality and Accuracy In Results

The benefits of neural networks involve high quality and accuracy in outputs. Your human workforce, no matter how many times they check for errors, can still leave some flaws unnoticed and that s what you want to eliminate as the CEO of your company. You need accuracy and quality in every big and small task. Humans can get tired and they need rest due to which sometimes they make careless mistakes while working.

These software robots do not need sleep or rest and they will not make any kind of mistake. These networks utilize your company’s information, gather data from the web, information from its own work, and function in order to develop new, improved understandings and provide you with high quality and accurate results.

By using deep learning these robots can easily recognize more images and data, understand spoken language, remove problems and perform efficiently.

Through improvements done by intelligent automation, your human workers might get worried about their jobs but actually, they have greater potential for growth.

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3) Job Growth

Jobs growth is also one of the most common neural network advantages. Companies that use machine learning must have to train their workers to use the software. This actually means growth for the right human worker.

Your other employees can easily concentrate on more important things while doing their daily work and without being distracted by these time consuming and repetitive and tasks that you can handover to AI. When your human workforce is relaxed and at ease, they will find more time to create and improve their work performance which can lead to the rapid growth of your organization.

These advantages of artificial neural networks are appealing enough for any business to implement machine learning so as to improve their business performance and enhance their growth process. There is a tough competition out there which makes it hard for businesses to survive and strive but with the use of advanced technology and intelligent automation, organizations can easily maintain their place in the industry.

FAQs

Why neural networks are used in 2020?

Deep learning provides endless opportunities for businesses in order to grow and improve their business operations. Through intelligent automation and using deep learning, great changes can be bought in daily life activities. There are still debates on AI and data ethics, however, businesses are relying more and more every day on advanced technology as their valuable resource for survival as there are various advantages of neural networks. With growing competition, neural networks are only paving the way for businesses to grow faster and work better.

What is the advantage of deep learning?

The biggest benefit of deep learning is that it is able to execute featuring engineering on its own. In a deep learning approach, the data is scanned by an algorithm in order to identify features that correlate and later combine them in order to promote fast learning.

Advantages of neural networks over machine learning?

Deep learning focuses on unsupervised learning. To be better said, deep learning utilizes machine learning algorithms that are able to improve without constant help from a human. Deep learning is able to do this by using artificial neural networks. But deep learning is not entirely dependent upon ANN. However, there are some aspects that take inspiration from ANN.

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Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

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Best Celeb Deepfakes

Celeb DeepFakes – Uncover The Dark Side of Artificial Intelligence

Celeb DeepFakes – Uncover the Dark Universe of Artificial Intelligence

Muhammad Imran

Author 

March 2, 2020

Deepfakes Celeb exists in real world. Seek the greatest examples in this guide.

Best Celeb Deepfakes

DeepFakes are fake versions of recordings that appear exactly like the real thing. Celeb DeepFakes utilizes machine learning solutions and computer vision technologies to draw deceptive capabilities for visual or audio content manipulation. People have been utilizing this to produce fictional, yet highly convincing photos and videos. Even audio is being deepfaked by creating “voice clones” of famous personalities. To many, this technology can be really helpful while others have used it to wreak havoc.

What are Celeb Deepfakes?

General public was made aware of the deepfake technology in 2017, when an anonymous Reddit user posted fake adult videos featuring famous celebrities like Scarlett Johansson. These were not real-life footage but celebrity faces and adult videos were combined using deepfake to make the videos appear real. Over the years the technology has picked up where the algorithm does not require ample video footage, which is only available for public figures to create fake videos.

Learn how Fraud detection solution will help out in detecting false patterns.

How are DeepFakes Celeb Made?

Creation of Face-swap videos involve a series of steps. It starts with running thousands of face shots of two people through the encoder (AI algorithm). The encoder finds similarities and differences between the two and compresses the images to combine the common features in the two faces. 

Then a decoder is trained to recover the faces from the compressed images. A different decoder is used for each face. The final step involves feeding encoded images to the “wrong” decoder, for instance feeding the images of person A to the decoder trained on person B. The decoder uses the expression and orientation of person A to  reconstruct the face of person B. In order to produce a convincing video, the process needs to be repeated for each frame.

Best Celeb DeepFakes Images

Check out this collage of Chris pine morph into some shape shifting alien 🙂 Lol.

Deepfakes celeb

Best Celeb DeepFakes Videos

Artist Bill Posters in response to Facebook’s refusal to remove Nancy Pelosi’s video, posted a fake video of Mark Zuckerberg in which he can be seen claiming to use Facebook users.

This video, Better Call Trump: Money Laundering 101 is based on a scene from Breaking Bad, whereby introducing Trump as James Gill.

A deepfake video was uploaded by Mystere Giraffe in which Will Smith’s face was plastered on Cardi’s body. It looked very real and was shared by both the celebrities on their social media accounts.

Best Celeb DeepFakes FAQs:

How are celeb DeepFakes made for free?

People download apps like FakeApp generally and look at how-to tutorials online to train their computers to pull off face-swaps. Users in China can use easy solutions like Zao, whereas Carica is another app that creates deepfakes in a matter of seconds.

What software is used for DeepFakes?

Deep learning is a set of intelligent algorithms that can learn and make decisions on their own. DeepFake employs AI and Machine Learning as a service to create content. The method also involves training generative neural network architectures.

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