Introducing ImageAI (v2.0.1) , the simple and convenient to use computer vision and deep learning python library. The new features contained in this release are:

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- Addition of Custom Image Prediction model trainings using SqueezeNet, ResNet50, InceptionV3 and DenseNet121

- Addition of Custom Image Prediction with custom trained models and generated model class json

- Preview Release: Addition of Video Object Detection and Video Custom Objects Detection (object tracking)

- Addition of file, numpy array and stream input types for all image prediction and object detection tasks

- Addition of file and numpy array output types for object detection and custom object detection in images

- Introduction of 4 speed modes ('normal', 'fast', 'faster' and 'fastest') for image prediction, allowing prediction time to reduce by 50% at 'fastest' while maintaining prediction accuracy
- Introduction of 5 speed modes ('normal', 'fast', 'faster', 'fastest' and 'flash') for all object detection and video object detection tasks, allowing detection time to reduce by over 80%
with 'flash' detection accuracy balanced with 'minimum_percentage_probability' which is kept at low values

- Introduction of rate of frame detections, allowing developers to adjust intervals of detections in videos in favour of real time/close to real time result.

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The new release, example codes, guides and documentations are available now on GitHub.

https://github.com/OlafenwaMoses/ImageAI

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This new release of ImageAI allows you to build production ready systems, be it in apps, web server or any other ecosystem you are developing.

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ImageAI will eventually support broader, deeper and more powerful computer vision tasks in future updates.

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We are open to suggestions and questions on the project, and we will be glad to look into them and respond appropriately.

ImageAI is an AI Commons project.

https://commons.specpal.science