Janani-V/pcb-defect-yolov8m-dspcbsd

YOLOv8m — DsPCBSD+ Defect Detection

Model Summary

• —Model: YOLOv8m
• —Task: PCB defect detection (object detection)
• —Dataset: DsPCBSD+ (via Roboflow export)
• —Classes: 9 defect categories
• —Framework: Ultralytics YOLOv8
• —Input size: 640 × 640
• —Training hardware: Google Colab, Tesla T4 GPU
• —Validation mAP@0.5: 0.839
• —Validation mAP@0.5:0.95: 0.508
• —Companion module: inspector.py — adds severity, root cause, impact, and recommended action per detection

🔍 Part of a two-stage project: see also Janani-V/pcb-defect-yolov8s-deeppcb — a simpler 6-class baseline on DeepPCB.

Model Comparison — Stage 1 vs Stage 2

Note: These two models were trained on different datasets (DeepPCB vs. DsPCBSD+) with different class counts and difficulty levels, so this is not a strictly apples-to-apples benchmark — it's meant to help you choose the right model for your use case.

Why the difference in scores?

DsPCBSD+ is a meaningfully harder benchmark than DeepPCB:

• —More classes (9 vs. 6) increases inter-class confusion risk
• —Class imbalance is more pronounced (spur: 929 instances vs. short: 169, a ~5.5x gap)
• —Higher intra-class variability — especially for conductor_scratch and conductor_foreign_object, which vary widely in size, shape, and appearance
• —DeepPCB's defects are more visually distinct and the dataset itself is smaller and cleaner by design

A lower mAP on DsPCBSD+ does not mean this model is "worse" — it reflects a genuinely harder detection problem with more real-world defect diversity.

Which model should you use?

• —Use the YOLOv8s / DeepPCB model if your defects match DeepPCB's 6 categories (open, short, mousebite, spur, copper, pin-hole) and you're working with grayscale linear-scan imagery — it's faster and more accurate for that specific defect set.
• —Use the YOLOv8m / DsPCBSD+ model (this repo) if you need broader defect coverage, including hole breakout, conductor scratches, and foreign object contamination — categories DeepPCB doesn't cover at all.

Model Description

This repository hosts a YOLOv8m object detection model fine-tuned on DsPCBSD+, a large-scale (10,259 image, 20,276 annotation) PCB surface defect dataset covering 9 defect categories across conductors, holes, and base material. This is a substantially harder detection task than DeepPCB — defects are smaller, more varied in shape/scale, and the class distribution is imbalanced.

Alongside the detection weights, this repository includes inspector.py, a companion knowledge-base module providing explanation, severity, root cause, impact, and recommended action per detected defect. This is a deterministic rules layer, not the model itself generating text — best.pt outputs class, bounding box, and confidence only.

Architecture: YOLOv8m (Ultralytics), single-stage anchor-free object detector Base weights: yolov8m.pt (COCO-pretrained, then fine-tuned)

Defect Classes

Evaluation Results

Validation set: 2,051 images, 4,092 annotated instances (official 8:2 train/val split).

Overall

Per-Class

Performance Chart

[image]

Training Curves

[image]

Confusion Matrix

[image]

Sample Detections

Sample 1 <table> <tr><th>Input</th><th>Prediction</th></tr> <tr><td><img src="sample1input.jpg" width="300"/></td><td><img src="sample1predicted.jpg" width="300"/></td></tr> </table>

Sample 2 <table> <tr><th>Input</th><th>Prediction</th></tr> <tr><td><img src="sample2input.jpg" width="300"/></td><td><img src="sample2predicted.jpg" width="300"/></td></tr> </table>

Training Configuration

Usage

from huggingface_hub import snapshot_download
import sys

local_dir = snapshot_download(repo_id="Janani-V/pcb-defect-yolov8m-dspcbsd")
sys.path.append(local_dir)

from inspector import PCBDefectInspector

inspector = PCBDefectInspector(weights_path=f"{local_dir}/best.pt")
result = inspector.inspect("your_pcb_image.jpg")

print(result["summary"])
for f in result["findings"]:
    print(f["class"], "-", f["severity"], "-", f["action"])

Limitations

• —conductor_foreign_object (mAP50 0.701) and conductor_scratch (mAP50 0.731) are the weakest classes — these defects have high intra-class variability in size, shape, and color, making them inherently harder to detect consistently.
• —Trained on 226×226-native PCB crop images; performance on full, un-cropped board images has not been separately validated.
• —Class distribution is imbalanced (spur: 929 instances vs. short: 169); rare-class performance may vary more across different data splits.
• —The inspector.py explanations are drawn from a static, hand-curated knowledge base — general guidance, not image-specific diagnosis.
• —Research/baseline model — not validated for production deployment without further testing on real manufacturing data.

Repository Contents

• —best.pt — fine-tuned YOLOv8m weights
• —inspector.py — companion module with severity/root-cause/impact/action knowledge base
• —metrics_chart.png — per-class performance chart
• —results.png — training loss/metric curves across all epochs
• —confusion_matrix.png — normalized confusion matrix across all 9 classes
• —sample*_input.jpg / sample*_predicted.jpg — example detections
• —README.md — this file

Author

Fine-tuned and maintained by [Janani-V](https://huggingface.co/Janani-V).

Citation

Original dataset: Lv, S. et al. "A dataset for deep learning based detection of printed circuit board surface defect." Scientific Data 11, 811 (2024). https://doi.org/10.1038/s41597-024-03656-8

Dataset access: janani-v-sdspd/dspcbsd-plus on Roboflow Universe

Model architecture: Jocher, G. et al. — Ultralytics YOLOv8: https://github.com/ultralytics/ultralytics