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README.md

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+---
+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 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](https://github.com/pnthang04/PCB-Prune-YOLO) before loading:
+
+```python
+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]`.