| license: cc-by-4.0 | |
| library_name: onnx | |
| pipeline_tag: object-detection | |
| tags: [object-detection, onnx, yolo11, rt-detr, gemstone, quality-control] | |
| # GemScan inclusion detectors (ONNX) | |
| YOLO11s and RT-DETR-L fine-tuned to detect inclusions in diamond photos, exported to ONNX | |
| (opset 17, static 1×3×640×640 input, FP32). `manifest.json` lists each file with its | |
| architecture, input size and class names. | |
| Test split (16 images, 117 inclusions): | |
| | Model | Precision | Recall | mAP50 | mAP50-95 | | |
| |---|---:|---:|---:|---:| | |
| | YOLO11s | 0.390 | 0.213 | 0.187 | 0.059 | | |
| | RT-DETR-L | 0.357 | 0.282 | 0.195 | 0.067 | | |
| Trained on [Diamond Inclusion](https://universe.roboflow.com/diamond-classification-data/diamond-inclusion) (CC BY 4.0) by Diamond Classification Data. | |
| Pre/post-processing that reproduces Ultralytics exactly is in `app/inference.py` of the source repository. | |