Initial commit: Wood knot detection model and GUI

MODEL_COMPARISON.md

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+# Model Framework Comparison
+
+## License Comparison
+
+| Framework | License | Commercial Use | OAK-D Support |
+|-----------|---------|----------------|---------------|
+| **RT-DETR** | Apache 2.0 | ✅ Free | ⭐ Excellent |
+| **YOLOv6** | MIT | ✅ Free | ⭐ Excellent |
+| **YOLOX** | MIT | ✅ Free | ⭐ Excellent |
+| RF-DETR | Check repo | ⚠️ Unknown | ⚠️ May need conversion |
+| YOLOv8/v11 | AGPL-3.0 | ❌ Paid ($1k-5k/yr) | Excellent |
+
+## Performance on OAK-D 4 Pro (48 TOPS INT8)
+
+| Model | Size | Speed (FPS) | Accuracy | Training Time |
+|-------|------|-------------|----------|---------------|
+| RT-DETR r18 | ~15MB | 30-40 | Good | Fast |
+| RT-DETR r34 | ~30MB | 20-30 | Better | Medium |
+| YOLOv6n | ~10MB | 40-50 | Good | Fast |
+| YOLOv6s | ~20MB | 30-40 | Better | Medium |
+| YOLOX nano | ~6MB | 50-60 | Good | Fast |
+| YOLOX-s | ~18MB | 35-45 | Better | Medium |
+
+## Which to Choose?
+
+### For Maximum Speed (50-60 FPS):
+**YOLOX nano** - Smallest, fastest, proven
+
+### For Best Balance (30-40 FPS):
+**RT-DETR r18** or **YOLOv6n** - Modern, accurate
+
+### For Best Accuracy (20-30 FPS):
+**RT-DETR r34** or **YOLOv6s** - Larger models
+
+### Recommended Starting Point:
+**YOLOv6n** - Great balance, proven OAK compatibility, MIT license
+
+## Training Commands
+
+All models use the same workflow in the GUI, or from command line:
+
+### RT-DETR
+```bash
+.venv/bin/python train_rtdetr.py \
+    --dataset-dir dataset_prepared \
+    --model rtdetr-r18 \
+    --epochs 100
+```
+
+### YOLOv6
+```bash
+.venv/bin/python train_yolov6.py \
+    --dataset-dir dataset_prepared \
+    --model yolov6n \
+    --epochs 100
+```
+
+### YOLOX (YOLOv8 equivalent)
+```bash
+.venv/bin/python train_yolox.py \
+    --dataset-dir dataset_prepared \
+    --model yolox-nano \
+    --epochs 100
+```
+
+## Export for OAK-D
+
+All models export to OpenVINO format for OAK deployment:
+
+```bash
+# RT-DETR
+.venv/bin/python export_rtdetr_oak.py --weights runs/rtdetr_training/training/weights/best.pt
+
+# YOLOv6/YOLOX use Ultralytics export
+.venv/bin/python -c "
+from ultralytics import YOLO
+model = YOLO('runs/yolov6_training/training/weights/best.pt')
+model.export(format='openvino', imgsz=640, half=False)
+"
+```
+
+Then convert to blob:
+- Online: https://blobconverter.luxonis.com/
+- CLI: `blobconverter --openvino-xml model.xml`
+
+## Tips
+
+1. **Start with nano/r18 models** for fast iteration
+2. **Train for 100-200 epochs** - use early stopping
+3. **Collect 200+ images** for good accuracy
+4. **Test on OAK-D** before collecting more data
+5. **Use INT8 quantization** for full 48 TOPS speed