Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,64 @@
+---
+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 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:
+
+```python
+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]`.