CodeProject.AI Server allows analysis modules to be downloaded and installed at runtime. This project contains the list, and downloads, of each module that is currently available.
Introduction
CodeProject.AI Server includes an ecosystem of modules that can be downloaded and installed at runtime.
This article serves as a reference list, but also as the source for downloadable modules for CodeProject.AI Server. To install one of these modules, simply install CodeProject.AI Server and head to the "Install modules" tab of the dashboard. Choose your module. hit "Install" and you're set.
Note that modules are tagged by platform and system, so if the system you are on (for instance a Raspberry Pi, and arm64 macOS machine, or an x64 Windows machine) doesn't match the supported platforms, then that module will not be available.
Want to create your own module? It's easy! We have examples on adding a Python module and a .NET module, but the concepts extend to any language that provides the means for scripted installation. Let your imagination go nuts.
Modules
Supporting CodeProject.AI Server 2.7.
Computer Audition
- Sound Classifier (TensorFlow)
v1.3.3 All Python, TensorFlow
The sound classifier uses Tensorflow with Python to classify sound files based on the UrbanSound8K dataset.
Computer Vision
- License Plate Reader
v3.2.2 All except Windows-arm64 Python, PaddlePaddle
Detects and readers single-line and multi-line license plates using YOLO object detection and the PaddleOCR toolkit
By Mike Lud
- License Plate Reader (RKNN)
v1.4.1 Orange Pi Radxa ROCK Python, FastDeploy
Detects and readers single-line and multi-line licence plates. This module only works with Rockchip RK3588/RK3588S NPUs like the Orange Pi 5/5B/5 Plus or Radxa ROCK.
By Mike Lud
- Object Detection (Coral)
v2.3.4 All Python, TensorFlow-Lite
The object detection module uses the Coral TPU to locate and classify the objects the models have been trained on.
Project by Chris Maunder, Seth Price, based on
Coral.ai examples.
- Object Detection (YOLOv5 .NET)
v1.10.2 All except Windows-arm64 C#, ONNX, DirectML, YOLO
Provides Object Detection using YOLOv5 ONNX models with DirectML. This module is best for those on Windows and Linux without CUDA enabled GPUs
Project by Matthew Dennis, based on
yolov5-net.
- Object Detection (YOLOv5 3.1)
v1.10.0 All except macOS Python, PyTorch, YOLO
Provides Object Detection using YOLOv5 3.1 targeting CUDA 10 or 11 for older GPUs.
Project by Chris Maunder, Matthew Dennis, based on
Deepstack.
- Object Detection (YOLOv5 6.2)
v1.9.2 All except Raspberry Pi Jetson
Provides Object Detection using YOLOv5 6.2 targeting CUDA 11.5+, PyTorch < 2.0 for newer GPUs.
Project by Matthew Dennis, based on
Ultralytics YOLOv5.
- Object Detection (YOLOv5 RKNN)
v1.7.1 Orange Pi Radxa ROCK Python, FastDeploy, YOLO
Provides Object Detection using YOLOv5 RKNN models. This module only works with Rockchip RK3588/RK3588S NPUs like the Orange Pi 5/5B/5 Plus
By Mike Lud
- Object Detection (YOLOv8)
v1.5.0 All Python, PyTorch, YOLO
Provides Object Detection in Python>=3.8 using YOLOv8. Great for newer NVIDIA GPUs
Project by Chris Maunder, based on
ultralytics.
- Optical Character Recognition
v2.1.1 All except Windows-arm64 Python, PaddlePaddle
Provides OCR support using the PaddleOCR toolkit
By Mike Lud
- Scene Classification
v1.7.2 All except Jetson Python, PyTorch
Classifies an image according to one of 365 pre-trained scenes
Project by Chris Maunder, Matthew Dennis, based on
Deepstack.
Face Recognition
- Face Processing
v1.11.0 All except Jetson Python, PyTorch
A number of Face image APIs including detect, recognize, and compare.
Project by Chris Maunder, Matthew Dennis, based on
Deepstack.
Generative AI
- LlamaChat
v1.6.0 All except Windows-arm64 Raspberry Pi Orange Pi Radxa ROCK Jetson Python, Llama
A Large Language Model based on the Machine Learning Compilation for LLMs
Project by Chris Maunder, based on
llama-cpp-python.
- Text to Image
v1.2.2 Windows macOS Linux Python, PyTorch, Stable Diffusion
Generates an image from a text prompt.
Project by Matthew Dennis, based on
Diffusers.
Image Processing
- Background Remover
v1.10.1 All except Linux Raspberry Pi Orange Pi Radxa ROCK Jetson Python, ONNX
Automatically removes the background from a picture
Project by Chris Maunder, based on
rembg.
- Cartoonizer
v1.6.0 All except Raspberry Pi Orange Pi Radxa ROCK Jetson Python, PyTorch
Convert a photo into an anime style cartoon
Project by Chris Maunder, based on
animegan2-pytorch.
- Portrait Filter
v2.0.0 Windows Windows-arm64 C#, ONNX, DirectML
Provides a depth-of-field (bokeh) effect on images. Great for selfies.
Project by Matthew Dennis, based on
C# PortraitModeFilter.
- Super Resolution
v2.1.2 All Python, ONNX
Increases the resolution of an image using AI
Project by Chris Maunder, based on
PyTorch.org example.
Natural Language
- Sentiment Analysis
v2.0.0 Windows macOS C#, TensorFlow
Provides an analysis of the sentiment of a piece of text. Positive or negative?
Project by Matthew Dennis, based on
.NET ML Samples.
- Text Summary
v1.8.1 All Python, NLTK
Summarizes text content by selecting a number of sentences that are most representative of the content.
Project by Chris Maunder, based on
Github gist.
Training
- Training for YoloV5 6.2
v1.6.5 All except Raspberry Pi Orange Pi Radxa ROCK Jetson Python, PyTorch, YOLO
Train custom models for YOLOv5 v6.2 with support for CPUs, CUDA enabled GPUs, and Apple Silicon.
Project by Matthew Dennis, based on
Ultralytics YOLOv5.
Using the modules
All modules are downloaded and installed via the CodeProject.AI Server dashboard, under the 'modules' tab
Chris Maunder is the co-founder of
CodeProject and
ContentLab.com, and has been a prominent figure in the software development community for nearly 30 years. Hailing from Australia, Chris has a background in Mathematics, Astrophysics, Environmental Engineering and Defence Research. His programming endeavours span everything from FORTRAN on Super Computers, C++/MFC on Windows, through to to high-load .NET web applications and Python AI applications on everything from macOS to a Raspberry Pi. Chris is a full-stack developer who is as comfortable with SQL as he is with CSS.
In the late 1990s, he and his business partner David Cunningham recognized the need for a platform that would facilitate knowledge-sharing among developers, leading to the establishment of CodeProject.com in 1999. Chris's expertise in programming and his passion for fostering a collaborative environment have played a pivotal role in the success of CodeProject.com. Over the years, the website has grown into a vibrant community where programmers worldwide can connect, exchange ideas, and find solutions to coding challenges. Chris is a prolific contributor to the developer community through his articles and tutorials, and his latest passion project,
CodeProject.AI.
In addition to his work with CodeProject.com, Chris co-founded ContentLab and DeveloperMedia, two projects focussed on helping companies make their Software Projects a success. Chris's roles included Product Development, Content Creation, Client Satisfaction and Systems Automation.