👋 Greetings Everyone!
I'm Onuralp, a seasoned Senior Machine Learning Engineer Lv2 @Ultralytics with expertise in Python 🐍, Kotlin 📱, C++ ⚙️, and Rust 🦀. My passion lies in the dynamic fields of Computer Vision 👁️, Machine Learning 🤖, and Deep Learning 🧠. Beyond development, I have a strong foundation in DevOps and MLOps, ensuring seamless deployment, automation, and scalability of cutting-edge solutions 🚀.
I am a long-term Contributor and Mindshare Member, Fedora KDE SIG Member, Fedora Website and Apps Team Member, and RPM Packager at the Fedora Project. I am also an Organizer and Speaker at GDG Samsun, where I share insights on Deep Learning, Machine Learning, Computer Vision, and Vision-Language Models (VLMs).
In addition to my community work, I contribute to a variety of FLOSS and OSS projects across GitHub, GitLab, and Pagure always aiming to build together and make open source a better place for everyone.
On the backend, I primarily use Python, while on the mobile side, I work with Kotlin and Flutter, leveraging Google technologies such as ARCore, MediaPipe, Firebase, FlatBuffers, ONNX Runtime, and ncnn.
On the ML side, I work extensively with Ultralytics, where I am a Senior Machine Learning Engineer, developing and maintaining cutting-edge computer vision tools and YOLO models. My primary deep learning framework is PyTorch, which I use for model training, fine-tuning, and deployment workflows.
I also contribute to related open-source projects such as ExecuTorch an efficient on-device inference framework from PyTorch, and ONNXSlim a lightweight ONNX optimization toolkit.
In addition, I have experience with other frameworks like TensorFlow, TensorBoard, Keras, and JAX, which I occasionally use for experimentation and cross-framework integration.
I also use the Unity engine for creating AR and ML applications.
In addition to my diverse skill set, I am actively contributing and co-maintaner to a computer vision project named sahi is an open-source project that provides a simple and efficient way to perform slicing-based inference for object detection models. It is designed to work with various deep learning frameworks and models, making it a versatile tool for computer vision tasks. In past I was a co-maintaner to Supervision. Supervision is dedicated to crafting reusable computer vision tools tailored to a wide array of needs. These projects are embodies my commitment to advancing the field of computer vision, offering robust solutions that empower developers and researchers alike.
I am also contributing various FLOSS and OSS projects around GitHub/Gitlab/Pagure and other platforms to make everyone happy and doing better projects together.
I’ve proudly participated in Hacktoberfest every year since 2022, completing four consecutive years (2022, 2023, 2024, 2025).
Through this journey, I’ve contributed dozens of PRs, met inspiring developers from around the world, and grown both technically and personally.
💚 For me, Hacktoberfest isn’t just about code — it’s about people, learning, and giving back to the community. I’m also part of the Holopin x Hacktoberfest: 10 Badge Club, celebrating those who’ve contributed continuously since 2022. And yes, somewhere out there, a tree is growing in my name — a small but meaningful reminder that code can make a difference beyond the screen 🌍.
I’m always open to collaborating, contributing, or just talking about open source, computer vision, and AI.
Feel free to reach out or check out my latest work here on GitHub! 🚀
Here is my Arsenal Tools and Skills I use;
- Ultralytics - The world's leading YOLO framework for object detection, instance segmentation, pose estimation, and image classification. Building state-of-the-art computer vision models for real-world applications.
- Ultralytics Inference - 🦀 High-performance YOLO inference library written in Rust with ONNX Runtime support. Provides a fast, safe, and efficient interface for running YOLO models with an API designed to match the Ultralytics Python package. (Main Maintainer)
- TrackForge - A unified, high-performance computer vision tracking library implemented in Rust with Python bindings. Features state-of-the-art algorithms like ByteTrack and DeepSORT, optimized for speed and ease of use across both ecosystems. (Main Maintainer)
- ExecuTorch - PyTorch's efficient on-device inference framework for edge and mobile devices. Contributing to enable ML model deployment on resource-constrained platforms.
- YOLO-World - Real-time open-vocabulary object detection model. Enabling YOLO to detect any object with text prompts without additional training.
- SAHI - Slicing Aided Hyper Inference framework for object detection on large images. Co-maintainer helping to improve inference quality on high-resolution imagery. (Co-Maintainer)
- GFPGAN-ncnn-vulkan - Real-world face restoration powered by GFPGAN, optimized with ncnn and Vulkan for efficient cross-platform inference.
- CvCamera-Mobile - Template Mobile computer vision camera project for Android, integrating OpenCV and ML capabilities for real-time processing.
- Supervision - Reusable computer vision tools for working with detections, annotations, and datasets. Former co-maintainer helping build the foundation for CV workflows.
- Supervision Conda Forge - Conda package distribution for Supervision, making it accessible to the scientific Python community.
- Sceneview-android - SceneView for Android - ARCore and 3D rendering made easy.
- Sceneform-android - Sceneform Maintained - ARCore SDK for Android from the SceneView team.
- Nvidia Auto Installer - Automated NVIDIA driver installation tool for Fedora Linux, simplifying GPU setup for the community.
If you want to stay in touch with me, these links can be useful.
- AskFedora account
- Fedora Pagure Account
- Src.Fedora Account
- Credly
- Google Developer Account
- Personal Fedora wiki page
- Fedora Contribution Badges
- Google Cloud Skill Boost Profile
- HuggingFace
