license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo
- yolov8
- depgraph
- torch-pruning
- structured-pruning
- deeppcb
datasets:
- thangkt/PCB-Prune-YOLO-DeepPCB
metrics:
- map
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].