thangkt/PCB-Prune-YOLO-P20-Direct

PCB-Prune-YOLO P20 Direct

Validation-selected YOLOv8n checkpoint produced by direct DepGraph structured pruning followed by 50-epoch matched fine-tuning on DeepPCB. No sparse learning, knowledge distillation, or test-set model selection was used.

Precision	Recall	mAP50	mAP50-95
0.96214	0.96186	0.98184	0.76710

The model has 1,913,971 parameters and 2.5722 GMACs, reductions of 36.46% and 36.85% from baseline. Its static batch-1 TensorRT FP16 engine reaches 1.933 ms (517.45 FPS) on Tesla T4 with validation mAP50-95 0.76931.

Training used direct local group-magnitude pruning at ratio 0.20 without channel rounding, followed by AdamW (lr0=0.001, lrf=0.01, weight decay 0.0005), batch 64, patience 10, seed 42, AMP, and deterministic mode.

Structured pruning changes the architecture. Clone and install the project before loading:

from ultralytics import YOLO
model = YOLO("best.pt")
results = model("pcb.jpg", imgsz=640)

New-process CUDA inference was verified with output [1,10,8400].