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CodeProject.AI Server Modules Registry

4.89/5 (5 votes)
9 Aug 2024CPOL4 min read 31.4K   627K  
A registry for downloadable modules for installation in CodeProject.AI Server
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 2.8.

Computer Audition

  • Sound Classifier (TensorFlow)
    v1.4.0 All Python, TensorFlow
    The sound classifier uses Tensorflow with Python to classify sound files based on the UrbanSound8K dataset.
    Project by Chris Maunder, based on Tensorflow-Audio-Classification.

     

Computer Vision

  • License Plate Reader
    v3.3.0 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)
    v1.5.0 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)
    v2.4.0 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)
    v1.11.0 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)
    v1.11.0 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)
    v1.10.0 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)
    v1.8.0 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)
    v1.6.0 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
    v2.2.0 All except Windows-arm64 Python, PaddlePaddle
    Provides OCR support using the PaddleOCR toolkit
    By Mike Lud

     
  • Scene Classification
    v1.8.0 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
    v1.12.0 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
    v1.7.0 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
    v1.3.0 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
    v1.11.0 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
    v1.7.0 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
    v2.1.0 Windows 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
    v2.2.0 All Python, ONNX
    Increases the resolution of an image using AI
    Project by Chris Maunder, based on PyTorch.org example.

     

Natural Language

  • Sentiment Analysis
    v2.1.0 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
    v1.9.0 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
    v1.7.0 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

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)