thangkt/PCB-Prune-YOLO-P30-Direct

PCB-Prune-YOLO P30 Direct

Strongest compression candidate in the direct DepGraph study. This validation-selected YOLOv8n checkpoint uses structured pruning followed by the same 50-epoch fine-tuning configuration as P10/P20. No knowledge distillation or test-set model selection was used.

Precision	Recall	mAP50	mAP50-95
0.95324	0.94374	0.97788	0.75030

The model has 1,452,562 parameters and 1.9619 GMACs, reductions of 51.77% and 51.83% from baseline. Its static batch-1 TensorRT FP16 engine reaches 1.754 ms (569.97 FPS) on Tesla T4, about 1.05x the TensorRT baseline throughput, with validation mAP50-95 0.75610.

Training used direct local group-magnitude pruning at ratio 0.30 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].