Translating a Complex Object Detection Model for Sales Teams: An AI Documentation Case Study
Source: Dev.to
The Challenge
Original technical paragraph from the engineers:
Our proprietary detection framework implements a multi‑scale feature pyramid network with deformable convolutions and focal loss optimization. The backbone utilizes an EfficientNet‑B4 architecture pretrained on ImageNet, fine‑tuned using mixed precision training with the AdamW optimizer. We’ve achieved state‑of‑the‑art mean Average Precision (mAP) of 0.87 on the internal benchmark dataset, with inference latency of 17 ms on our edge hardware, making it suitable for real‑time detection tasks in constrained computational environments.
The paragraph is dense with jargon and metrics—accurate but inaccessible to a sales team.
My Approach
- Identify the audience: Sales team members who need clarity and confidence to explain AI to clients.
- Focus on key aspects: Accuracy, speed, and real‑world limitations.
- Translate step by step: Rewrite each sentence in plain, conversational language without losing meaning.
- Add a visual analogy: Provide a memorable comparison to help explain how the model works.
- Create a translation glossary: Simplify recurring technical terms for easy reference.
The Result
Sales‑Friendly Rewrite
This object detection model has a high reliability rate for real‑time use and is designed to assist the average human when driving. It reacts fast enough to keep up with real‑world driving conditions in supported environments.
Visual Analogy
It’s like an extra pair of eyes that assists you in identifying objects in real time while driving.
Translation Glossary
| Technical Term | Plain‑Language Explanation |
|---|---|
| Multi‑scale Feature Pyramid Network | Lets the system notice both big and small objects at the same time. |
| Deformable Convolutions | Helps the system adjust to unusual or stretched shapes so it can recognize them better. |
| EfficientNet‑B4 Backbone | The “engine” of the system that efficiently extracts important details from images. |
| Focal Loss Optimization | A training technique that makes the model focus on harder‑to‑detect objects. |
| Mixed Precision Training | Uses a combination of high‑ and low‑precision numbers to speed up learning without losing accuracy. |
| AdamW Optimizer | An advanced method for adjusting the model’s parameters during training, leading to better performance. |
| Mean Average Precision (mAP) 0.87 | A measure of overall detection accuracy; 0.87 indicates very high performance. |
| Inference latency 17 ms | The time it takes the model to process an image; 17 ms is fast enough for real‑time use. |