Abraia VisionDeveloper toolkit

Developer toolkit

Build vision applications with the Abraia Vision SDK

A Python toolkit for image processing, model training, inference, video pipelines, and edge deployment. Use it to turn a tested workflow into application code.

Capabilities

Tools for the whole application

The SDK supports both ready-made model workflows and custom development.

Train and curate

Prepare image datasets, review quality findings, annotate, and train classification, detection, or segmentation models.

Read SDK documentation →

Run and compose

Use model inference with video, tracking, counting, region timing, and configurable ordered pipelines.

Explore runtime tools →

Deploy on edge hardware

Run compatible ONNX models and compile supported detection or segmentation checkpoints for Hailo targets.

Explore deployment →

Studio and SDK

Move from visual workflow to Python code

Vision Studio is the desktop workbench for dataset review, annotation, spectral analysis, training, and pipeline previews. The SDK gives developers Python APIs and a command-line interface for custom applications.

Install the package with pip install -U abraia. Some models and hardware targets need optional dependencies. Studio also works with multispectral and hyperspectral scenes; explore multispectral imaging solutions.

Vision Studio pipeline editor used to prototype computer vision workflows

Face recognition

An SDK capability for specific applications

The SDK includes face representation and identification for matching faces against a reference set in images or video. The application still needs enrollment, evaluation on representative footage, and integration designed for its setting.

See the current SDK documentation and examples for API details. For people counting without identification, see queue monitoring.

Example image with identified faces from the Abraia Vision SDK

Bring your vision problem to us

Tell us about the images, sensor, operating environment, and decision you want the system to support. We can identify a practical first evaluation.