thangkt/PCB-Prune-YOLO-Baseline

PCB-Prune-YOLO YOLOv8n Baseline

YOLOv8n baseline trained on DeepPCB to detect six PCB defect classes: open, short, mousebite, spur, copper, and pin-hole.

Validation results

The best checkpoint was selected on the validation split at epoch 98 of 100.

These are validation results. The official test split should only be evaluated for the final report.

Training configuration

• —Model: YOLOv8n pretrained checkpoint
• —Image size: 640
• —Global batch size: 128 (64 per GPU)
• —Hardware: 2x Tesla T4
• —Maximum epochs: 100
• —Best epoch: 98
• —AMP: enabled
• —Seed: 42

Usage

from ultralytics import YOLO

model = YOLO("best.pt")
results = model.predict("pcb.jpg")

The checkpoint is stored as best.pt. args.yaml and results.csv contain the training configuration and epoch history.

Source code: https://github.com/pnthang04/PCB-Prune-YOLO