# 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