👋 Jesús Royeth

Computer vision engineer

detection · segmentation · tracking · optimization · edge AI · cloud AI

I work where computer vision models meet production: architectures, custom layers and heads,
tracking pipelines, quantization, and deployment on constrained hardware.

🔭 Open source

I contribute performance improvements, bug fixes, and hardware support to widely used computer vision libraries. Recent work includes extending Hailo support across seven YOLO tasks, fixing RKNN INT8 exports that produced zero detections, making an RF-DETR DataLoader up to 1.67× faster, and reducing Hungarian matcher time by up to 77%.

	Merged PRs
ultralytics/ultralytics	106
roboflow/rf-detr	54
roboflow/trackers	12
roboflow/supervision	2
SpikeInterface/spikeinterface	1

🏷️ My contributions have been credited by name in 38 upstream releases.

🚀 Selected work

🟣 Ultralytics YOLO — 7 highlights

Extended Ultralytics' Hailo backend from detection-only to seven tasks across the complete .pt → ONNX → INT8 → HEF export path. #25254 · #25259 · #25276 · #25280 · #25283 · #25348

Fixed RKNN INT8 exports across detection, segmentation, pose, and OBB that produced valid files but no detections. #25524

Made channels-last the automatic default for native PyTorch inference on x86 CPUs, improving median throughput by 18.43% for YOLO26n and 14.09% for YOLO11n. #25983

Fixed dataset fraction sampling that selected images from a single class, raising YOLO11n-cls top-1 accuracy from 9.89% to 87.64% in the validation run. #25968

Restored ByteTrack's low-confidence recovery step, which was unreachable under the default configuration and discarded detections in the 0.1–0.25 confidence range. #25034

Removed per-object GPU stalls from result processing, making Results.plot() 7.55× faster and cutting segmentation-validator synchronizations from 876 to 2. #25230 · #25853

Found train/test contamination affecting 27.9% of KITTI depth training images; validation found no statistically significant accuracy impact. #25650

🟠 RF-DETR — 6 highlights

Reduced the DataLoader handoff from 114 objects to 9 per batch, improving throughput by up to 1.67× and preventing file-descriptor crashes at high worker counts. #1399

Rewrote the evaluation confidence sweep from O(T·N) to O(N log N), making it 7.1–7.8× faster at COCO scale with identical macro_f1 results. #1339

Reduced Hungarian matcher time and peak CUDA memory by up to 77% by padding to the largest target count in each batch instead of the total number of targets. #1297

Corrected the encoder mask-loss computation across all released segmentation models and added the missing tests for SegmentationHead. #1331

Aligned ONNX/TFLite decoding with PyTorch, restoring 30 detections dropped across 26 of 200 COCO validation images. #1320

Preserved callback state after resuming training, preventing diverged runs from overwriting good checkpoints with NaN weights. #1318

🔵 Trackers — 4 highlights

Fixed a float-equality bug that degraded steady-frame-rate tracking by as much as 4.86 HOTA and 236 extra ID switches on DanceTrack. #531

Fixed the CLI, demo, and benchmark paths silently assuming 30 fps—a default that was wrong for every SportsMOT and DanceTrack sequence supported by the benchmark. #569

Fixed OC-SORT omitting low-confidence inputs from its return value, restoring one output row per detection. #566

Reduced a Kalman filter noise-calculation hot path by 64.93% while preserving bit-identical output. #572