YOLOv11 Computer Vision & Object Detection System

A confidential deep learning research project utilizing state-of-the-art YOLOv11 computer vision architecture for high-speed object detection, spatial tracking, and automated image feature classification.

Key Highlights

Confidential Research · YOLOv11 · Christ University

Confidential research project supervised at Christ University
YOLOv11 neural network backbone optimized for low-latency visual inference
Custom dataset annotation, feature extraction, and bounding-box evaluation
Designed for real-time edge hardware deployment and spatial analysis

Tech Stack & Tools Used

YOLOv11

PyTorch

Python

Computer Vision

OpenCV

Deep Learning

01. Research & Design

Formulated object detection objectives and prepared annotated image datasets for YOLOv11 fine-tuning.

02. Model Training

Trained YOLOv11 detection models with custom hyperparameters, data augmentations, and loss functions.

03. Evaluation

Benchmarked mean Average Precision (mAP) and inference latency across diverse testing conditions.

04. Outcome

Shipped a high-accuracy computer vision pipeline for academic research applications.

Outcome

Trained and evaluated custom YOLOv11 object detection pipelines for specialized vision benchmarks.

Demonstrates end-to-end software execution, clean UI polish, and practical problem solving.

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