Issues when a module needs root access to install (Linux/macOS only at this point)
Better output when installing
Additions to module settings schema
Release 2.3 Beta
A focus on improving the installation of modules at runtime. More error checks, faster re-install, better reporting, and manual fallbacks in situations where admin rights are needed
A revamped SDK that removes much (or all, in some cases) of the boilerplate code needed in install scripts
Fine grained support for different CUDA versions as well as systems such as Raspberry Pi, Orange Pi and Jetson
Support for CUDA 12.2
GPU support for PaddlePaddle (OCR and license plate readers benefit)
CUDA 12.2 Docker image
Lots of bug fixes in install scripts
UI tweaks
ALPR now using GPU in Windows
Corrections to Linux/macOS installers
Release 2.2 Beta
An entirely new Windows installer offering more installation options and a smoother upgrade experience from here on.
New macOS and Ubuntu native installers, for x64 and arm64 (including Raspberry Pi)
A new installation SDK for making module installers far easier
Improved installation feedback and self-checks
Coral.AI support for Linux, macOS (version 11 and 12 only) and Windows
Release 2.1 Beta
Improved Raspberry Pi support. A new, fast object detection module with
support for the Coral.AI TPU, all within an Arm64 Docker image
All modules can now be installed / uninstalled (rather than having some modules fixed and uninstallble).
Installer is streamlined: Only the server is installed at installation time, and on first run we install Object Detection (Python and .NET) and Face Processing (which can be uninstalled).
Reworking of the Python module SDK. Modules are new child classes, not aggregators of our module runner.
Reworking of the modulesettings file to make it simpler and have less replication
Improved logging: quantity, quality, filtering and better information
Addition of 2 modules: ObjectDetectionTFLite for Object Detection on a on Raspberry Pi using Coral,
and Cartoonise for some fun
Improvements to half-precision support checks on CUDA cards
Modules are now versioned and our module registry will now only show modules that fit your current server version.
Various bug fixes
Shared Python runtimes now in runtimes.
All modules moved from the AnalysisLayer folder to the modules folder
Tested on CUDA 12 (Note: ALPR and OCR do not run on CUDA 12)
Release 2.0 Beta
New Downloadable module system
Re-introduction of PyTorch 1.7 YOLO module for older GPUs
.NET 7
Release 1.6.0.0 Beta
Optimised RAM use
Ability to enable / disable modules and GPU support via the dashboard
REST settings API for updating settings on the fly
Apple M1/M2 GPU support
Async processes and logging for a performance boost
Breaking: the CustomObjectDetection is now part of ObjectDetectionYolo
Release 1.5.6.2 Beta
Docker NVIDIA GPU support
Further performance improvements
cuDNN install script to help with NVIDIA driver and toolkit installation
Bug fixes
Release 1.5.6 Beta
NVIDIA GPU support for Windows
Perf improvements to Python modules
Work on the Python SDK to make creating modules easier
Dev installers now drastically simplified for those creating new modules
Added SuperResolution as a demo module
Release 1.5 Beta
Support for custom models
Release 1.3.x Beta
Refactored and improved setup and module addition system
Introduction of modulesettings.json files
New analysis modules
Release 1.2.x Beta
Support for Apple Silicon for development mode
Native Windows installer
Runs as Windows Service
Run in a Docker Container
Installs and Builds using VSCode in Linux (Ubuntu), macOS and Windows, as well as Visual Studio on Windows
General optimisation of the download payload sizes
Previous
We started with a proof of concept on Windows 10+ only. Installs we via a simple BAT script, and the code has is full of exciting sharp edges. A simple dashboard and playground are included. Analysis is currently Python code only
Version checks are enabled to alert users to new versions
A new .NET implementation scene detection using the YOLO model to ensure the codebase is platform and tech stack agnostic