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