PCBInspect-TextDefect

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

Defect classifier for crops routed as Text (silkscreen). Binary: defect-free vs wrong part printed.

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, 480, 480) RGB, normalized /255, NCHW; single output output0 (1, 2) raw logits (apply softmax yourself for probabilities).
• Classes (2), index order = labels.json: Golden, WrongPart.

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=94):

class	precision	recall	f1	support
Golden	1.000	1.000	1.000	53
WrongPart	1.000	1.000	1.000	41

test (top-1 1.000, macro-F1 1.000, n=97):

class	precision	recall	f1	support
Golden	1.000	1.000	1.000	53
WrongPart	1.000	1.000	1.000	44

Limitations

Smallest training set of the four (476 images total). Test top-1/macro-F1 both 1.000 (n=97), but the small, low-diversity test set means this number should be treated cautiously rather than as a tight confidence interval.

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((480, 480))
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)