ImageAI

State-of-the-art Recognition and Detection AI with few lines of code.

Made by Moses Olafenwa & John Olafenwa.

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ImageAI is an easy to use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find below features supported, links to official documentations as well as articles on ImageAI


Traction

ImageAI is widely used around the world by professionals, students, research groups and businesses.



3000+ GitHub Stars

Actively developed, maintained and supported since the 2018.



75,000+ Developers

Empowering tens of thousands of developers around with state-of-the-art AI tools and frameworks.



500,000+ Media Outreach

A robust ecosystem of comprehensive tutorials, documentations and example codes.



60+ Countries

Bringing AI technology, tools and knowledge to individuals, teams, corporate organizations and institutions around the world.




Features



Image Recognition

ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet.






Object Detection

ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. The model implementations provided include RetinaNet, YOLOv3 and TinyYOLOv3.






Video Detection and Analysis

ImageAI provides an extended API to detect, locate and identify 80 objects in videos and retrieve full analytical data on every frame, second and minute. This feature is supported for video files, device camera and IP camera live feed.






Custom Recognition Training

ImageAI provides API to train new image recognition models on new image datasets for custom use cases. It also provides implementations to integrate and deploy the custom image recognition models.





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