Instance Segmentation from Auto-Labeling to Edge Deployment

Project overview

Area	Details
Goal	Convert unlabeled images into an edge-deployable instance-segmentation model
Teacher models	SEEM and SAMv2
Deployment model	YOLOv11 with post-training quantization

I developed the full path from foundation-model-assisted annotation to a smaller deployable model. The workflow separates expensive offline label generation from efficient runtime inference, while retaining class-aware masks throughout the conversion.

Auto-labeling pipeline

• Used SEEM to generate rough masks with class labels.
• Converted rough masks into bounding-box prompts.
• Used SAMv2 to generate cleaner segmentation masks from those prompts.
• Modified SAMv2 output with an additional classifier to attach class labels to predicted masks.

Training and deployment

• Trained a deployable YOLOv11 model using the generated labels.
• Applied post-training quantization for faster edge-device inference.

Engineering value

The pipeline makes dataset expansion repeatable: new imagery can pass through the same teacher-model stages, be reviewed, and feed the next deployment-model iteration. This creates a practical bridge between general-purpose foundation models and product-specific edge inference.