PCB Inspection Suite
Collection
Datasets and YOLO26 models for automated PCB inspection: component detection, bare-board defect detection, and solder/assembly defect detection. • 9 items • Updated
PCB Component Classifier
A Random Forest over 33 hand-engineered computer-vision features that classifies discrete electronic components — resistors, capacitors, diodes, inductors, transistors and ICs — from a single image, and can explain why it reached each decision.
No GPU, no pretrained backbone, ~50 ms per image on a laptop CPU.
Part of the PCB Inspection Suite. Trained on ParsaVictor/pcb-component-classifier. Full pipeline, feature extraction code, and the explanation renderer: GitHub: pcb-component-classifier.
Results
Trained on 1,195 labelled images across 6 classes. Evaluated on a stratified 25% test split, and on a 120-image holdout set never used in training or tuning.
| Evaluation | Accuracy |
|---|---|
| Test split (299 samples) | 92.98% |
| Holdout set (120 images) | 93.33% (112/120) |
Per-class performance (test split)
| Class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| Resistors | 0.93 | 1.00 | 0.96 | 154 |
| Transistors | 1.00 | 0.90 | 0.95 | 21 |
| ICs | 0.97 | 0.88 | 0.92 | 32 |
| Capacitors | 0.90 | 0.92 | 0.91 | 59 |
| Inductors | 0.88 | 0.78 | 0.82 | 18 |
| Diodes | 1.00 | 0.60 | 0.75 | 15 |
| Weighted avg | 0.93 | 0.93 | 0.93 | 299 |
Diodes are the honest weak spot — perfect precision but 0.60 recall on only 15 test samples (visually close to small capacitors, under-represented class).
Confidence as a review gate
| Threshold | Accepted | Accuracy on accepted | Flagged for review | Errors caught |
|---|---|---|---|---|
| none | 100% | 93.3% | 0 | 0 / 8 |
| 0.50 | 88% | 98.1% | 14 | 6 / 8 |
| 0.60 | 79% | 98.9% | 25 | 7 / 8 |
| 0.70 | 66% | 98.7% | 41 | 7 / 8 |
Accepting only predictions above 0.60 confidence leaves 98.9% accuracy on the 79% it handles automatically, and routes 7 of the 8 errors into the fifth it hands to a human.
Usage
import joblib
from huggingface_hub import hf_hub_download
weights = hf_hub_download("ParsaVictor/pcb-component-classifier", "tree_rf.joblib")
clf = joblib.load(weights)
# Requires the 33-feature extractor from the GitHub repo (src/pcb_classifier/features.py)
# pred = clf.predict([features])
See the GitHub repo for the full feature-extraction pipeline and the six-panel decision-path explanation renderer.
Files
tree_rf.joblib— trained on isolated component photos (this dataset, 93.3% holdout accuracy)feature_importance.csv— the 33 features ranked by importance
Related
- Training dataset: pcb-component-classifier
- Training/inference code: GitHub: pcb-component-classifier
- Sibling model (board-level detection): pcb-component-detection-yolo26 — also ships a variant of this classifier (
tree_rf_boards.joblib) retrained on training-board crops - Full suite overview: pcb-inspection-suite
License
MIT.