Building a Modular Computer Vision Inference Service: Lessons from Spec-AI
Spec-AI is a commercial image-analysis project that evaluates visible product packaging against authentic reference examples. This article discusses general engineering patterns and deliberately excludes proprietary scoring rules and model parameters.
Why a multi-stage pipeline?
A single classifier is not always sufficient when the evidence includes packaging appearance, visible text, and object geometry. A modular approach makes it possible to inspect different sources of evidence and handle missing or low-quality inputs explicitly.
High-level processing
flowchart TD
A[Client image] --> B[Authenticated API]
B --> C[Inference execution]
C --> D[Image and region processing]
D --> E[Visual and text evidence]
E --> F[Model-based assessment]
F --> G[Structured response]
The reviewed implementation uses FastAPI, segmentation, OCR, a shared visual feature extractor, brand identification, and product-specific anomaly assessment. Its API can execute directly or dispatch work through Celery and Redis. In the configured worker mode, the HTTP request still waits for the completed task, so this is not a non-blocking submission API.
Model lifecycle
Model files are managed separately from application source through DVC. A central engineering concern is artifact compatibility: changing a feature extractor can invalidate downstream embeddings and anomaly models unless those assets are rebuilt together.
Deployment architecture
The repository contains a GPU-oriented Docker deployment workflow with GitHub Actions, a container registry, DigitalOcean, and DVC-managed model assets. This is evidence of an implemented deployment configuration—not proof that every deployment succeeded or that the service meets a specific availability target.
Lessons and open engineering work
- Readiness checks should verify meaningful inference capability, not only web-process startup.
- Model-asset synchronization should fail safely when required artifacts are missing.
- Queued inference should have explicit timeouts and clear client-facing execution semantics.
- Model evaluation must use representative held-out samples; a similarity score is not automatically a calibrated probability.
No accuracy, throughput, or latency figures are presented without independent measurement.