Glenn Jocher
Founder & CEO at Ultralytics
Madrid, Community of Madrid, Spain
Summary
🤩
Rockstar🎓
Top SchoolGlenn Jocher is the founder and CEO of Ultralytics with nine years of hands-on experience building state-of-the-art vision AI, best known for leading development of the YOLOv5 and YOLOv8 object detection families and the widely used ultralytics repositories. Rooted in a background of physics and defense-focused simulation, he brings uncommon depth in Monte Carlo modeling and sensor systems from prior roles supporting the NGA and Army programs. He combines research-grade rigor with product-first engineering to make vision models deployable across PyTorch, ONNX, CoreML and TFLite, and his open-source work includes practical tooling like JSON2YOLO for real-world dataset preparation. Based in Madrid, he is driven by a long-term vision of using vision AI as a building block toward AGI, blending curiosity about fundamental physics with a relentless focus on reliable, production-ready ML.
9 years of coding experience
6 years of employment as a software developer
United States Naval AcademyBachelor of Science - BS Aerospace Engineering, Bachelor of Science - BS Aerospace Engineering at Purdue University
English, Spanish
Stackoverflow
86reputation
17kreached
2answers
1question
Github Skills (22)
json10
pytorch10
python10
machine-learning10
onnx10
numpy10
datasets10
trainings10
tensorflow10
model-optimization10
data-processing10
object-detection10
computer-vision10
tflite10
modeling10
Programming languages (16)
Github contributions (5)
ultralytics/ultralytics
Sep 2022 - Mar 2023
Ultralytics YOLO11 🚀
Role in this project:
ML Engineer
ML EngineerContributions:4 releases, 337 reviews, 280 commits in 6 months
Contributions summary:Glenn primarily focused on developing and improving the YOLOv8 object detection model. The contributions include implementing new training arguments to fine-tune the model, as well as updating the processing of masks and keypoints for improved performance. In addition to these improvements, the user integrated the model's results with metrics.
pytorchdeep-learningyolov8object-detectiononnx
ultralytics/JSON2YOLO
May 2019 - Aug 2022
Convert JSON annotations into YOLO format.
Role in this project:Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:2 releases, 1 review, 84 commits in 3 years 4 months
Contributions summary:Glenn's contributions primarily revolve around converting JSON annotation files into the YOLO format. Their work involves modifying the `run.py` file to parse data from JSON files, normalize bounding box coordinates, and write the data in the YOLO annotation format. The user implemented changes to handle different datasets, including supermarket2 and infolks, and incorporated features to split the dataset into train, test, and validation sets. These changes demonstrate a focus on preparing and processing data for object detection tasks.
annotationscocodatasetlabelboxobject-detection
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