How to Choose the Right Model
When you’re using AI for tasks like quality checks in manufacturing, it’s important to pick the model that best fits what you want the AI to do. Think of it like choosing the right tool for a job.
Start with These Questions:
What do I want the AI to do / What is my goal?
- Do I want it to identify what’s in an image?
- Or do I want it to locate exactly where something is?
- Or maybe I want it to outline the shape of a defect?
Do I need exact shapes or just rough locations?
- Are there multiple objects, overlapping parts, or tiny defects?
- Are the defects local or spread across the entire surface?
Match the Task to the Model
Quick way to match your inspection goal with the right type of AI model:
| 🎯 Task | 📋 Description | ✔️ Best Model Type |
|---|---|---|
| Classification | Decide if the whole image is OK or defective | Image Classifier |
| Detection | Find and count defects or parts (shows boxes) | Object Detector |
| Segmentation | Trace the shape or area of a defect (pixel-level detail) | Segmentation Model |
General Rule of Thumb – Choosing the Right AI Tool
- Use classification when the entire image can be labeled as one thing — like “OK” or “Defective.” (Example: A part is completely fine or completely faulty.)
- Use detection when you need to find and count multiple things in one image.(Example: You want to know how many dents or missing screws are present, and where they are.)
- Use segmentation when you need the AI to draw the exact shape of the problem — not just point to it. (Example: You want to highlight the exact edges of a crack, rust, or paint defect.)
Documentation for Model Selection:
Below is a quick reference guide for selecting the appropriate model type based on input requirements and expected output.
Model Type Overview:
➤ Classification Models
Purpose: Look at the whole image and say what it is.
Input: One image.
Output: A label, like “defective” or “good.”
Use-case: You just want to know if a product is okay or not — no need to point out where the issue is.
Common AI Models: ResNet, EfficientNet, MobileNet.
Example:
➤ Object Detection Models
Purpose: Find and label multiple parts or problems in one image.
Input: One image.
Output: Boxes showing where the issues are and what they are.
Use-case: - You want to count how many dents, cracks, or missing parts there are. - You want to know exactly where they are.
Popular Architectures: YOLO, SSD, Faster R-CNN.
Example:
➤ Segmentation Models
Purpose: Assign a class to each pixel.
Input: Image.
Output: Pixel-wise mask (binary or multi-class).
Use-case: Precisely outline corrosion, paint issues, or micro-defects.
Popular Architectures: UNet, DeepLabv3+, PaDiM (for anomaly), Mask R-CNN.
Example:
Model Selection Checklist:
| Requirement | Recommended Model Type |
|---|---|
| Classify the entire image | Classification |
| Count or localize multiple elements | Detection |
| Capture shape or boundaries | Segmentation |
| Detect subtle or unknown anomalies | Segmentation (Anomaly Models e.g., PaDiM, PatchCore) |