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Hi, I'm Anatole Garnier, an AI Engineer passionate about computer vision and building high-performance models that solve real-world problems. I've had the opportunity to work on exciting projects across robotics, archaeology, and ecology, focusing on developing applications that improve precision and efficiency using AI technologies. I love collaborating on innovative R&D projects and staying at the forefront of deep learning and image processing techniques. When I'm not working on AI, I enjoy sports like volleyball and football, and I love traveling to cities like Tokyo, London, and Munich.…Hi, I'm Anatole Garnier, an AI Engineer passionate about computer vision and building high-performance models that solve real-world problems. I've had the opportunity to work on exciting projects across robotics, archaeology, and ecology, focusing on developing applications that improve precision and efficiency using AI technologies. I love collaborating on innovative R&D projects and staying at the forefront of deep learning and image processing techniques. When I'm not working on AI, I enjoy sports like volleyball and football, and I love traveling to cities like Tokyo, London, and Munich.

M. Garnier Anatole

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Hi, I'm Anatole Garnier, an AI Engineer passionate about computer vision and building high-performance models that solve real-world problems. I've had the opportunity to work on exciting projects across robotics, archaeology, and ecology, focusing on developing applications that improve precision and efficiency using AI technologies. I love collaborating on innovative R&D projects and staying at the forefront of deep learning and image processing techniques. When I'm not working on AI, I enjoy sports like volleyball and football, and I love traveling to cities like Tokyo, London, and Munich.…Hi, I'm Anatole Garnier, an AI Engineer passionate about computer vision and building high-performance models that solve real-world problems. I've had the opportunity to work on exciting projects across robotics, archaeology, and ecology, focusing on developing applications that improve precision and efficiency using AI technologies. I love collaborating on innovative R&D projects and staying at the forefront of deep learning and image processing techniques. When I'm not working on AI, I enjoy sports like volleyball and football, and I love traveling to cities like Tokyo, London, and Munich.

Available to hire

Hi, I’m Anatole Garnier, an AI Engineer passionate about computer vision and building high-performance models that solve real-world problems. I’ve had the opportunity to work on exciting projects across robotics, archaeology, and ecology, focusing on developing applications that improve precision and efficiency using AI technologies.

I love collaborating on innovative R&D projects and staying at the forefront of deep learning and image processing techniques. When I’m not working on AI, I enjoy sports like volleyball and football, and I love traveling to cities like Tokyo, London, and Munich.

Skills

Python

PyTorch

SQL

Experience Level

Python

Expert

PyTorch

Expert

Intermediate

SQL

Intermediate

Language

French

Fluent

English

Fluent

German

Intermediate

Work Experience

Research and Development Engineer at LaBRI & Pollen Robotics

December 31, 2025 - July 22, 2025

Developed a real-time object segmentation module for robotic prosthetics. Utilized an attention mechanism in Fast SAM to enhance precision while reducing latency. Achieved segmentation in 12 ms per image, enabling smooth operation in real-world conditions. Focused the attention mechanism on generating precise masks within bounding boxes, optimized for robotic use.

Application Development for Object Segmentation at Université de Bordeaux & Argus

December 31, 2025 - July 22, 2025

Developed an application for detecting pollinating insects to monitor species robustness in their environment. Implemented a YOLO model for real-time segmentation and tracking. Improved detection accuracy by over 60% for flowers and insects compared to the initial proof of concept.

Sequential Segmentation Application Development at Université de Bordeaux & Archéovision

December 31, 2024 - July 22, 2025

Developed a segmentation application for recognizing blue pigments on ancient stones from the Temple of Apollo in Delphi. Employed a U-Net network and Dice Loss for improved segmentation. Enhanced accuracy by 30% compared to the initial proof of concept, facilitating archaeological analysis.