These samples should help you get started in DL based projects. The main purpose in both samples is to isolate the model code from the pipeline that would use its services. They serve as a toy deployment example in a desktop environment.
There are two samples in this repository: python and c++. Essentially they
are both simple AI based video analysis tools. They both support multiple
tasks:
- python: detection, segmentation, pose estimation
- c++: detection, segmentation
Python Detectron2 Segmentation https://github.com/D-K-E/mlsamples/assets/22693287/9013a8dd-0c29-4d8e-8380-929cbd2aed7c
Python Yolov8 Segmentation https://github.com/D-K-E/mlsamples/assets/22693287/009c200b-6f03-4123-95fe-5b1011c31153
C++ Yolov8 Detection https://github.com/D-K-E/mlsamples/assets/22693287/d6c2e986-01f9-4e3f-bf84-4828136053c1
Python version supports multiple backends:
- YOLOv8
- Detectron2
C++ version supports only YOLOv8.
Python version automatically downloads needed models, but C++ version requires
them to be present in the assets directory.
Though C++ and Python is using the same backend model YOLOv8, they produce
different outputs. This is due to preprocessing and post processing functions
involved prior and after the inference. The python version uses ultralytics
library to leverage preprocessing/postprocessing. The C++ version implements
it from scratch with some help from various open sourced libraries.
Each repository contains its proper build instructions. We are also providing
test and reference videos in case the user wants to not only inspect the
source code but also launch the program and see what it provides.
These videos can be found in the data folder.
All samples are tested with Ubuntu 22.04.