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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 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](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]`.