PCBInspect-Region

Part of the SentinelPCB defect-inspection router: a region classifier dispatches each component ROI crop to a region-specific defect classifier. Sibling repos: PCBInspect-Region, PCBInspect-BodyDefect, PCBInspect-LeadDefect, PCBInspect-TextDefect.

Companion structural-feature detector (unrelated task — detects MountingHole/ComponentBody/ SolderJoint/Lead, not defects): PCBInspect-AI.

Role

First stage. Given a component ROI crop, predicts which physical region it is (Body, Lead, Text) so the pipeline can dispatch to the matching defect classifier. Near-trivial task — the three regions look visually distinct.

Model

• Base: yolo26n-cls (Ultralytics), classification head, fine-tuned on AOI component-ROI crops.
• Export: ONNX, opset 17, no NMS (classification only) — single input images (1, 3, 224, 224) RGB, normalized /255, NCHW; single output output0 (1, 3) raw logits (apply softmax yourself for probabilities).
• Classes (3), index order = labels.json: Body, Lead, Text.

Data

Trained on a proprietary AOI dataset of SMT component-ROI crops (paired defect-free reference + defective capture per physical site), not publicly released. Split is grouped by physical capture site (never by raw image) so a component's reference and defect crop never straddle train/val/test.

Metrics

val (top-1 1.000, macro-F1 1.000, n=2004):

class	precision	recall	f1	support
Body	0.999	1.000	1.000	1174
Lead	1.000	0.999	0.999	738
Text	1.000	1.000	1.000	92

test (top-1 1.000, macro-F1 1.000, n=2002):

class	precision	recall	f1	support
Body	0.999	1.000	1.000	1164
Lead	1.000	0.999	0.999	736
Text	1.000	1.000	1.000	102

Limitations

None observed; effectively solved on held-out data (n=2,002 test).

Usage

import onnxruntime as ort
import numpy as np
from PIL import Image

sess = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
img = Image.open("crop.jpg").convert("RGB").resize((224, 224))
x = (np.asarray(img, dtype=np.float32) / 255.0).transpose(2, 0, 1)[None, ...]
(logits,) = sess.run(None, {"images": x})
probs = np.exp(logits) / np.exp(logits).sum()
print(probs)