This tutorial details how to create APIs for detecting and recognizing any custom object using DeepStack AI Server

Computer vision has made it possible to develop applications and automation systems that have the ability to detect, identify and locate objects in images, videos and live camera feeds. With profound impact on many areas of socio-economic activity such security, manufacturing, quality assurance, IoT, autonomous vehicles e.t.c , today’s computer vision powered by deep learning models provide limitless opportunity to develop highly intelligent visual systems.

For most developers, researchers and IT engineers, the primary sources for state-of-the-art computer vision integrations are:

  • DNN…


The leading AI Engine on the edge now open source on GitHub

Our team at DeepQuest AI is glad to announce that DeepStack is now open source and the source code is now available on GitHub. With over 3.4 million installs on Docker Hub, DeepStack provides state-of-the-art AI APIs that can be hosted fully offline on a PC , edge device or on a private VM/cluster on the cloud. Supported platforms include

  • Linux OS
  • Windows
  • Mac OS
  • NVIDIA Jetson
  • CPU and NVIDIA GPUs

DeepStack currently provides APIs for the following AI features

  • face detection, recognition and matching
  • object detection
  • scene…


All versions of DeepStack AI Server are now free.

DeepQuest AI is proud to announce that DeepStack AI Server is now 100% free for both personal, research and commercial purposes. This means all versions which include the CPU, GPU and Raspberry Pi version can be installed and deployed without registration and activation key. Kindly learn more in the details provided below

Why make DeepStack free?


A tutorial on training an AI for counting and detecting damaged apples

It is an unquestionable fact that agriculture is the oldest and most important field of work in human history. As the global human population rises every year, the demand for agricultural food and produce increase as well. On the contrary, the rapid civilization experienced in nations and cities across the world continually shows a massive rural-to-urban migration, coupled with the shift in the nature of work where most people prefer to work in non-agricultural sectors. …


DeepStack is now available for the Raspberry Pi with APIs accelerated using Intel Neural Compute Stick.

We are glad to announce today that DeepStack AI Server that offers image recognition and detection AI APIs fully offline and on-the-edge is now available on the Raspberry Pi with its APIs accelerated by the Intel Neural Compute Stick. This release of DeepStack Pi is to allow easy deployment of AI for Home Automation and Industrial IoT, with all the benefits that comes with the AI server which are:

  • 100% privacy
  • Unlimited APIs
  • Zero API calls
  • Fully offline-capable

DeepStack Pi currently supports the following…


Step-by-step tutorial on training object detection models on your custom dataset

Object detection is one of the most profound aspects of computer vision as it allows you to locate, identify, count and track any object-of-interest in images and videos. Object detection is used extensively in many interesting areas of work and study such as:

  • autonomous vehicles
  • security
  • pedestrian/crowd detection
  • plate number and vehicle detection
  • industrial automation (E.g item picking and sorting)
  • robotics and more.

A number of pre-collected object detection datasets such as Pascal VOC, Microsoft’s COCO, Google’s Open Images are readily available along with their pre-trained models for detection…


Tutorial on annotating your custom image dataset in the Pascal VOC format

Object detection is one of the most fascinating aspect of computer vision as it allows you to detect each individual object in images, locate their position and size as well relative to the rest of the image. Today’s state-of-the-art object detection models are powered by Deep Learning and at the very soul of training deep learning networks is the training dataset.

Training dataset are images collected as samples and annotated for training deep neural networks. For object detection, their are many formats for preparing and annotating your dataset…


Step-by-step guide of training accurate models with limited number of images.

In the field of modern Artificial Intelligence, Deep Learning has proved to be single most accurate methods to train intelligent models which can match and effectively augment human intelligence in performing tasks and solving problems. Deep Learning models have been extensively used computer vision, Natural Language processing , Speech and robotics. To leverage Deep Learning in creating intelligent systems, the following uncompromising factors must be in place:

  • Sufficient (sometimes large) training data
  • High Performance Computing using NVIDIA GPUs
  • A well fine-tuned deep neural network.

Most researchers, developers and companies…


Step-by-step tutorial on creating your private AI APIs on the cloud.

The quest to build smarter applications and software systems has never been as paramount as it is in this era, where emerging technologies like Artificial Intelligence becomes a major differentiating factor between digital products that win the market and those that emerge and vanish like a “whales tail”. …


Global reach, case studies and future versions of ImageAI

It is with utmost excitement that I write this piece, not only because the ImageAI project has made incredible impact since it’s launch on the 22nd of March 2018, but because of the innovations that will be made possible by the project in years to come. It’s been been a year and 2 months since We announced ImageAI, a computer vision library we built to enable developers of any level of expertise to access and integrate state-of-the-art computer vision functionalities powered by deep learning.

Moses Olafenwa

Software Engineer @Microsoft . A self-Taught computer programmer, Deep Learning, Computer Vision Researcher and Developer. http://olafenwamoses.me

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