Blog2023-07-06T20:39:33+00:00

Reviving Black and White with Advanced Colorization Techniques

This is a guide to NAFNet, a machine learning model compatible with the imaged SDK. You can quickly develop AI applications using imaged SDK along with several other pre-built imaged models.

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Introduction to Colorful Image Colorization

Developed by researchers Richard Zhang, Phillip Isola, and Alexei A. Efros, the Colorful Image Colorization project is a pioneering endeavor that leverages deep learning to add vibrant colors to black and white images. Initially presented at ECCV in 2016, this technology has evolved, incorporating functionalities from their subsequent work on Real-Time User-Guided Image Colorization with Learned Deep Priors, showcased at SIGGRAPH 2017.

Enhancing Images with Deep Learning

The project offers an automatic colorization tool that transforms monochrome photos into colorful images. It intelligently predicts colors based on the content of the image, learning from a vast dataset of color images to apply realistic hues that bring life to old or originally black and white photos.

Conclusion

The Colorful Image Colorization project not only enhances visual media by adding color but also serves as a significant example of practical applications of deep learning in art and media restoration. It invites both tech enthusiasts and general users to explore the potential of AI in creative industries.

Technical Specs:

Test Environment: MacBook Pro, 2.6 GHz Intel Core i7, 16 GB RAM

  • Model Size: 129 MB
  • CPU Inference Time: ~200 ms
By |April 17, 2024|Categories: Model Intro|Tags: , , , |

Real-ESRGAN: A Robust Tool for Image and Video Enhancement

This is a guide to NAFNet, a machine learning model compatible with the imaged SDK. You can quickly develop AI applications using imaged SDK along with several other pre-built imaged models.

Introduction

Real-ESRGAN is a continuation and enhancement of the ESRGAN project, geared towards creating practical algorithms for general image and video restoration. This tool is particularly effective because it is trained solely on synthetic data yet achieves remarkable results in real-world applications.

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Real-World Applications

Real-ESRGAN shines in various restoration tasks, such as enhancing low-resolution images or refurbishing old videos. It provides tools for both casual users and developers, including portable executables and detailed Python scripts for custom applications. Its capability to handle animations makes it especially popular in the anime community, where it is used to enhance video quality and clarity.

Conclusion

Real-ESRGAN is not just a tool but a comprehensive framework for image and video enhancement. Whether you’re a researcher, a content creator, or just someone looking to improve the quality of your digital media, Real-ESRGAN offers the tools and flexibility to achieve impressive results. Explore it today to transform your images and videos with cutting-edge AI technology.

Technical Specs:

Test Environment: MacBook Pro, 2.6 GHz Intel Core i7, 16 GB RAM

  • Model Size: 67 MB
  • CPU Inference Time: ~8878 ms
By |April 17, 2024|Categories: Model Intro|Tags: , , , |

Revolutionizing Image Restoration: Introducing NAFNet

This is a guide to NAFNet, a machine learning model compatible with the imaged SDK. You can quickly develop AI applications using imaged SDK along with several other pre-built imaged models.

Introduction to NAFNet

In the dynamic realm of image restoration, the quest for efficiency and simplicity often leads to groundbreaking innovations. Enter NAFNet: the Nonlinear Activation Free Network, a robust solution designed to streamline and enhance the process of image restoration. This blog delves into the essence of NAFNet, exploring its capabilities, features, and how it sets new benchmarks in the field.

What is NAFNet?

Developed by Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun, NAFNet originated from a simple yet powerful idea: to create a computationally efficient baseline that surpasses state-of-the-art (SOTA) methods in image restoration. Officially introduced in their paper “Simple Baselines for Image Restoration,” presented at ECCV 2022, NAFNet challenges the conventional reliance on nonlinear activation functions like Sigmoid, ReLU, and GELU. Instead, it achieves superior results through simpler methods, such as multiplication or outright removal of these functions.

Key Features and Benefits

  1. High Efficiency: NAFNet demonstrates an impressive ability to reduce computational costs while improving performance. For instance, in image deblurring on the GoPro dataset, it achieves a 33.69 dB PSNR, surpassing previous methods by 0.38 dB with only 8.4% of the computational cost.
  2. Versatility: The framework is versatile across various tasks, including denoising, deblurring, and super-resolution. Its architecture is optimized to provide excellent results across different benchmarks, making it a universal tool for image restoration.
  3. Open Source and Accessible: The implementation is based on BasicSR, an open-source toolbox for image and video restoration tasks. NAFNet’s code and pretrained models are readily available for community use and further development.

Achievements and Recognition

NAFNet has not only set new standards in image restoration but also garnered significant acclaim. It was selected for an oral presentation at the CVPR 2022 NTIRE workshop and won the first place in the NTIRE 2022 Stereo Image Super-resolution Challenge. Such accolades underline its impact and effectiveness in the field.

Conclusion

NAFNet is more than just a tool; it’s a significant step forward in making image restoration more accessible, efficient, and effective. By eliminating the need for complex nonlinear activation functions and focusing on simplicity and performance, NAFNet paves the way for future innovations in the field. Whether you are a researcher, developer, or enthusiast in image processing, NAFNet offers a new lens through which to view and tackle image restoration challenges.

Technical Specs:

Test Environment: MacBook Pro, 2.6 GHz Intel Core i7, 16 GB RAM

1. Deblur

  • Model Size: 272.8 MB
  • CPU Inference Time: ~4154.8 ms

2. Restore

  • Model Size: 778,3 MB
  • CPU Inference Time: ~2568 ms per inference
By |April 16, 2024|Categories: Model Intro|Tags: , , , |

How to Try iMAGED SDK with Docker

If you’re interested in trying out our iMAGED SDK with Docker, we’ve outlined the simple steps below.

First, create an account on our website at imaged.dev. Once you’re logged in, navigate to the dashboard and click on the “Request New Token” button. From there, choose the 1-month free beta option and submit your request. We’ll review your request and send you an email once it’s been approved.

Next, install Docker on your computer. Then, download and uncompress the iMAGED container. Copy the token from your dashboard to the app/license.txt file. Using the terminal, run “docker-compose up”.

After that, create folders inside the volumes/gallery folder and place your JPG images with a .jpg file extension inside the created folders. Once you’ve done that, open your browser and go to localhost:5000, then click “analyze”. You can watch the progress by opening the Docker dashboard and expanding the iMAGED_container_v01 option. Click on the iMAGED container to view the logs.

Finally, sit back and wait for the analysis to finish. You can even enjoy a cup of tea while you wait! Once your images have been analyzed, you can search for them by text or similar image.

By following these simple steps, you can easily try out our iMAGED SDK with Docker. Contact us if you have any questions or need assistance with the setup process. We’re here to help you get started and make the most of our powerful AI technology.

By |October 28, 2022|Categories: Article|

E-Commerce Product Search Using iMAGED

As online shopping becomes increasingly popular, retailers are looking for new ways to make it easier for customers to find and purchase the products they’re looking for. One promising technology is AI-powered image search, which allows shoppers to enter an image of something they’re looking for and receive a list of exact and similar matches.

According to Invesp, 74% of online shoppers believe text-based search is ineffective, and 72% say they regularly or always search for visual content before making a purchase. This consumer demand is driving the growth of the global visual search market, which is projected to reach almost $15 million by 2023, according to Predictly. Gartner also predicts that companies who adapt quickly and redesign their websites to support visual search will see a 30% increase in their digital revenue in 2021.

Major players like Amazon are already investing in this technology. In 2019, they launched StyleSnap, an AI-powered image search feature that allows shoppers to find fashion and home-decor items by using an image or screenshot. This feature not only helps customers find what they’re looking for but also fuels influencers who post their fashion finds on social media.

At iMAGED, we’re proud to offer our SDK, which enables retailers to create products and reference images that visually describe each product from a set of viewpoints. By adding these products to OpenSearch and Amazon Elasticsearch with our SDK, retailers can improve their product search capabilities and provide customers with more accurate results.

Our demo video showcases how our product search use case works. When users enter their own images, our SDK applies machine learning to analyze the image and compare it with the images in the retailer’s product database. It then returns a ranked list of visually and semantically similar results, making it easier for shoppers to find and purchase what they’re looking for.

By embracing AI-powered image search, retailers can improve the shopping experience for their customers and increase their digital revenue. Contact us to learn more about our SDK and how it can help your business.

By |October 23, 2022|Categories: Article|

Maintain value-added scenarios to grow your business mission

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By |October 20, 2022|Categories: Audit & Taxation|Tags: |

Streamline exceptional process & unleash your collaboration

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Driving success to your business

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By |October 20, 2022|Categories: Finance|Tags: |

Monetize proactive your e-business & access to accurate experiences

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Ut orci risus, accumsan porttitor cursus quis aliquet eget, justo. Sed pretium blandit orci. Ut eu diam at pede suscipit sodales. Aenean lectus eliti fermentum non convallis id, sagittis at, neque. Nullam mauris orci aliquet et iaculis au viverra vitae ligula. Nulla ut felis in purus aliquam imperdiet. Maecenas aliquet mollis lectus vivamus consectetuer risus et tortor.

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  • Supported business with intelligence

  • Private funds granted with help of Govt.

  • Increase efficiency and achieve better sales

Driving success to your business

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By |October 20, 2022|Categories: Business Relations|Tags: |

iMAGED SDK Now Available

We’re excited to announce that we’ve just released the first version of our iMAGED SDK! You can download it now and check out the documentation to learn more about its capabilities.

DownloadDocumentation

To see the SDK in action, we invite you to watch our demo video, which showcases how it can be used to implement an effective product search. With iMAGED, you can classify items or find similar items without the long training phase required by other solutions. It’s a drop-in replacement for AutoML, offering unparalleled ease of use and versatility.

Our goal is to provide businesses with cutting-edge AI technology that simplifies and streamlines their operations. With the iMAGED SDK, you’ll be able to unlock new possibilities for your business, whether you’re looking to improve product search or explore new applications for computer vision.

We’re proud to offer this innovative solution to our clients, and we look forward to seeing the exciting ways in which they use it to transform their operations. Try out the iMAGED SDK today and see for yourself what it can do for your business!

By |October 12, 2022|Categories: Announcement|

Reflecting Our Values

At IMAGED, an AI company in Rabat, Morocco, and Dubai, UAE, we’re committed to creating a smarter industry by harnessing the power of artificial intelligence and machine learning. Our goal is to develop innovative solutions that transform various domains, including manufacturing, diagnostics, safety, supply chains, commerce, cybersecurity, and healthcare.

With our deep learning technology, we’re constantly pushing the boundaries of what’s possible in these fields. Our products and services include training, consulting, technology licensing, and novel product development. We work closely with our clients to ensure that they’re maximizing the potential of AI and deep learning, so that they can achieve greater success and stay ahead of the competition.

At our core, we’re dedicated to providing advanced analytics for image and video, enabling businesses to gain valuable insights that help them make more informed decisions. Our team of experts is always exploring new ways to apply AI and machine learning, so that we can continue to drive innovation and help our clients reach new heights.

Partnering with us means embracing the power of artificial intelligence and deep learning to unlock new possibilities for your business. Let us help you transform your operations and achieve greater success than ever before.

By |October 6, 2022|Categories: Announcement|
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