PCB-Prune-YOLO P10 Direct
Validation-selected YOLOv8n checkpoint produced by direct DepGraph structured pruning followed by matched fine-tuning on DeepPCB. This model does not use sparse learning or knowledge distillation.
Validation results
| Precision | Recall | mAP50 | mAP50-95 |
|---|---|---|---|
| 0.96479 | 0.95706 | 0.98273 | 0.77736 |
At seed 42 this direct P10 control exceeded the matched sparse-learning P10 by 1.42 mAP50-95 percentage points. This is a single-seed observation and the DeepPCB test split was not used for model selection.
Compression and Tesla T4 benchmark
| Parameters | MACs | Size | Latency batch 1 | FPS |
|---|---|---|---|---|
| 2,416,871 | 3.2695G | 4.854 MiB | 10.433 ms | 95.85 |
Input size is 640. Latency uses 50 warm-up and 200 synchronized CUDA iterations.
Training configuration
- Direct local group-magnitude pruning, ratio 0.10, one step, no channel rounding
- AdamW,
lr0=0.001,lrf=0.01, momentum 0.9, weight decay 0.0005 - 50 epochs, batch 64, patience 10, seed 42, AMP and deterministic mode
- Six classes: open, short, mousebite, spur, copper, pin-hole
Loading
Structured pruning changes the serialized architecture. Install the project so
the PrunableC2f class is importable before loading:
from ultralytics import YOLO
model = YOLO("best.pt")
results = model("pcb.jpg", imgsz=640)
Project: https://github.com/pnthang04/PCB-Prune-YOLO
The checkpoint was verified by loading in a new process and running CUDA
inference with decoded output shape [1, 10, 8400].
- Downloads last month
- 7