Guided Demo

Computer Vision Segmentation Demo

What It Demonstrates

A visual walkthrough of image input, segmentation mask generation, overlay result, post-processing steps, and deployment considerations for production CV pipelines.

Who It Is For

Teams building medical, beauty, industrial, or inspection-related image analysis workflows.

Demo Flow

1. Upload or select a sample image
2. Run segmentation model (browser-based or API)
3. Display original image, predicted mask, and overlay
4. Show post-processing steps (morphological operations, contour extraction)
5. Deployment considerations (ONNX export, mobile optimization, API design)

Architecture

User -> Frontend -> API -> Preprocessing -> Model inference -> Post-processing -> Mask and overlay result

Tech Stack

OpenCV, PyTorch, ONNX, segmentation, Python, FastAPI, object storage.

Productionization Notes

• Sliding-window inference: Handling images larger than model input resolution
• Post-processing: Morphological operations and contour extraction for mask refinement
• Mobile deployment: TorchScript + JNI to avoid on-device Python runtime
• Production concerns: Auth, monitoring, retries, error handling, rate limits, model evaluation, scaling

CTA

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