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.
I contribute performance improvements, bug fixes, and hardware support to widely used computer vision libraries. Recent work includes being the most-credited community contributor to RF-DETR's 9× faster training, extending Hailo support across seven YOLO tasks, fixing RKNN INT8 exports that produced zero detections, and adding experimental Cloud TPU support to RF-DETR. Outside vision, I apply the same profiling approach to SpikeInterface's neuroscience pipelines.
| Merged PRs | |
|---|---|
| ultralytics/ultralytics | 108 |
| roboflow/rf-detr | 81 |
| roboflow/trackers | 18 |
| SpikeInterface/spikeinterface | 13 |
| roboflow/supervision | 2 |
| Borda/lucid-YOLO | 1 |
🏷️ My contributions have been credited by name in 43 upstream releases.
Credited in RF-DETR's write-up of its 9× faster training.
Busy is not the same as productive: RF-DETR now trains 9× faster on the same GPU
(AI Advances, September 2026) lists my matcher, optimizer, transformer, mask-loss, and
torch.compile pull requests among the contributions to the speedup.
Credited with "the bulk of this release's perf and correctness work" in the RF-DETR 1.10.0 release notes.
🟠 RF-DETR — 7 highlights
Cut AdamW step time by 85% and a full Nano training step by 23.5% by grouping parameters by learning rate and weight decay (465 parameter groups down to 28), with bit-identical weights. #1409
Rebuilt the Hungarian matcher's compact path, padding to the largest per-image target count and copying only diagonal cost blocks to the host, cutting matcher time by 51–77% and peak CUDA memory by up to 76%. #1281 · #1297
Made compile=True actually compile: it crashed with explicit single-scale training and was
silently a no-op under the default multi-scale setting.
#1411 ·
#1436
Brought RF-DETR training to Cloud TPU (experimental), fixing multi-device XLA strategy selection, padding targets to a fixed shape to avoid per-batch recompiles, and making the XLA test suite pass on real TPU silicon. #1426 · #1427 · #1450
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
Added an opt-in Inductor inference backend and removed all seven decoder graph breaks, cutting
batch-1 predict() latency by 47.7% on an L4.
#1425
Corrected the encoder mask-loss computation across all released segmentation models and added
the missing tests for SegmentationHead.
#1331
🟣 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
🔵 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
🟢 SpikeInterface — 4 highlights
Fixed spike amplitudes being scaled with channel gains as offsets, an error of +6,390 µV that flipped the sign of every spike on Intan recordings. #4765
Made amplitude scaling 2.41× faster on a 384-channel analyzer (9.23× on real Spike2 data), with bit-identical output. #4764 · #4786
Fixed synchrony metrics crediting the wrong units on multi-segment recordings while making them 8.84× faster and cutting peak memory from 1,052 to 316 MiB. #4770
Parallelized bad-channel detection, which now runs 4.30× faster on real 384-channel Neuropixels traces with identical labels. #4750
Frameworks & runtimes
Models & methods YOLO (v8 / 11 / 26) · RF-DETR · DINOv2 · SAM · deformable DETR · Kalman filtering and multi-object tracking
Silicon & platforms Hailo · MemryX MXA · RKNN · Cloud TPU · Google Cloud
