02 / SELECTED WORK
Featured projects.
Selected research and engineering projects focused on evaluation, deployment, and real-world system behavior.
01Edge AI
YOLO Edge Optimization
Took a YOLO detection model from research checkpoint to deployment across three accelerators and four runtimes, measuring what every optimization actually costs in latency, accuracy, and size. Found that export is 3.50× faster on NVIDIA TensorRT and 1.69× slower on Apple Core ML for the identical graph, and traced the regression to ten tensor-indexing operators in the NMS-free detection head that split the Core ML graph into eight partitions instead of five. Naive INT8 quantization collapsed the head to 0.000 mAP across all 5,000 COCO validation images without raising a single error; excluding 95 head nodes recovered it, and TensorRT quantized the same head successfully, establishing the failure as a quantizer limitation rather than an architectural one. Six findings documented alongside four hypotheses that failed under testing.
PyTorchYOLOONNX RuntimeCoreML
VIEW PROJECT →02Computer Vision
Structured Ablation of YOLOv8-L on VisDrone
Four modifications from the UAV small-object detection literature — attention, a stride-4 detection head, PIoU regression reweighting, and inference-time tiling — applied to one YOLOv8-L baseline under a single protocol. None beat the unmodified model. The most-recommended fix cost 0.148 mAP50 and reduced small-object recall by 31%.
PyTorchYOLOv8UltralyticsVisDrone2019-DET
VIEW PROJECT →03RAG / LLM Evaluation
RAG Evaluation Harness
Seven retrieval configurations benchmarked against a hand-verified eval set over a corpus of computer vision papers. No configuration beat plain dense retrieval on recall@1 — every added component improved recall@5 while making the top result worse. 14 findings, 10 of them negative.
PythonChromaDBsentence-transformersBM25
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