Computer vision model

YOLOv11 Lip Segmentation

A custom segmentation model trained on annotated lip images and exported for real-time computer-vision applications.

DatasetYOLOv11MasksExport

Segmentation pipelineModel outputs>95% validation

Case study

Problem, role, and solution.

A quick read of what had to change, where the engineering ownership sat, and how the final system answered the core project need.

Problem

Real-time vision applications need accurate region segmentation with practical model performance, not only general-purpose object detection.

My role

Dataset preparation, annotation workflow, model training, validation, export, and real-time application planning.

Solution

Collected and annotated a custom lip-segmentation dataset, trained a YOLOv11L-SEG model, evaluated validation accuracy, and exported the model for real-time use.

System evidence

Real screens from the YOLOv11 Lip Segmentation workflow.

These supporting visuals show the practical workflow, implementation details, and output quality behind the project.

YOLOv11 lip segmentation dataset preview showing annotated lip image samples prepared for model training. — Custom training data

YOLOv11 lip segmentation result showing a precise green contour mask around the lips. — Precise lip mask

Real-time lip segmentation inference preview with detected lip masks on real-world imagery. — Real-time segmentation

Lip segmentation example showing model handling across varied lip shapes and real-world conditions. — Variation handling

Technical profile

Stack, integrations, and build risks.

A compact read of the tools, connection points, and engineering constraints behind this case study.

8 tools

Technology stack

YOLOv11L-SEGPythonOpenCVPyTorchTensorFlowDeepFaceImage annotationSegmentation masks

3 links

Integrations

Computer-vision inference pipelinesModel export formatsRealtime application layer

3 risks

Engineering challenges

Preparing consistent segmentation annotations.Training for accuracy while keeping real-time use in mind.Showing measurable technical credibility without overstating business impact.

Outcomes

What changed after delivery.

3 verified results

Collected and annotated a custom lip-segmentation dataset.

Trained a YOLOv11L-SEG model with more than 95% validation accuracy.

Exported the model for real-time computer-vision applications.