I provide custom Computer Vision solutions using state-of-the-art deep learning techniques to solve real-world problems across multiple domains. My goal is to transform raw visual data into meaningful insights through accurate, efficient, and scalable AI models.

My services include image classification, object detection, image and semantic segmentation, face recognition, medical image analysis, and video analytics. I work extensively with modern computer vision and deep learning frameworks such as CNNs, Vision Transformers, YOLO, OpenCV, PyTorch, and TensorFlow to design models that meet both performance and deployment requirements. I focus not only on accuracy but also on robustness, efficiency, and real-world usability.

I can assist you throughout the entire machine learning pipeline, starting from dataset preparation and preprocessing (data cleaning, augmentation, annotation strategies) to model selection, training, evaluation, and optimization. I also support hyperparameter tuning, performance benchmarking, and model explainability to ensure your solution is reliable and interpretable. For deployment needs, I can help with model integration, optimization for inference, and deployment-ready workflows.

I work with researchers, startups, and enterprise teams, providing solutions tailored to experimental research, proof-of-concept prototypes, or full production-ready systems. Whether you need help validating a research idea, building an MVP, or deploying a scalable computer vision application, I ensure clean, well-documented code, reproducible results, and clear communication throughout the project.

If you are looking for a dependable computer vision expert to turn your idea into a working solution, I am ready to help.