Oleksander Zaitsev
Offer Oleksander work on your next project.
Private message
Offer work
Kyiv,
Ukraine
25 days back
a little busy
on the service 3 years
Rating
Successful projects
Average rating
Skills and abilities
Programming
Design & art
Portfolio
-
Emotion Analyzer
AI & Machine Learning
Emotional marketing for retail, services, and more. Training was conducted on the well-known dataset Expression in-the-Wild (ExpW). The number of classes - 7. The total number of images is 91,793. Training was carried out on Google Colaboratory using the library. Model architecture - YOLO11; Inference implemented in two variants in Python using the Ultralytics library, and in C++ using TensorRT for NVIDIA graphics cards, and for mini computers NVIDIA Jetson Nano. Two models are used for implementation. The first is face inference, the second is emotion inference, for NVIDIA models YOLO converted into engine engines. Ultralytics.
-
Training system of negative feedback NFTS
AI & Machine Learning
Application.
It is used in shooting training complexes as an autonomous shooting tool that independently makes decisions to take a shot. Targeting and decision-making are performed using computer vision algorithms with a pre-trained neural model.
Criteria.
The main criterion for making a shooting decision is the assessment of the shooter's (trainee's) position. The position assessment is a task that involves determining the location of certain points in the image, known as key points.
… Key points may represent various parts of the human body, such as joints, limbs, or other distinguishing features.
The result of the position assessment model is a set of points that represent the key points of the object in the image, along with reliability scores for each point.
Position assessment is the selection when you need to identify specific parts of the object and their location relative to each other.
Operating mode.
When the system determines the position of the shooter (trainee) as the person shooting, the system starts a countdown timer set by the operator, from 1 to 15 seconds.
After the time expires, if the shooter (trainee) has not changed their location, the system determines the center of the object along the X-axis, performs aiming, and takes the shot. If the shooter (trainee) has changed their location before the time set by the operator, the countdown is canceled, and the system enters standby mode.
Technical solution.
For training the position assessment model, the base model yolov8-pose was used, along with a video file of shooters and a pre-generated file with control points. Training was conducted on an NVIDIA graphics processor using Python and the Ultralytics library. To use the trained model on an NVIDIA Jetson microcomputer or on a computer with an NVIDIA graphics card, the #YOLO model file was converted to a tensorrt file. Inference is performed using the #OpenCV, #CUDA C++ libraries.
-
Project RH-1
C & C++
Development of control systems by robotic devices
The OpenCV
Use of the #NVIDIA Jetson Nano or RPi mini computer.
C++ programming language, #OpenCV CV v.4
… A screening device is a manipulator. Connect WiFi or BlueTooth.
Activity
| Latest proposals 1 | Budget | Added | Deadlines | Proposal | |
|---|---|---|---|---|---|
|
CUDA
11 USD
|
