Hadas S.

Responsibilities:

• Incremental Evaluation Engine-Developed an incremental evaluation engine that accurately detects changes between runs using hashing of samples, model, and configuration, and skips redundant inference. The engine uses a dual-layer caching mechanism — Local (SQLite) for fast lookups during execution, and Distributed (MinIO/S3) for persistence and sharing of results across runs and environments — reducing repeated run times by up to ~80%, with seamless integration across all Runners and task types.

• Runners & Dataset Adapters Architecture - Development of a modular evaluation architecture built on a dedicated Runner layer for each task (Classification / Detection / Segmentation), all sharing a unified run() interface while implementing task-specific execution logic. In parallel, a Dataset Adapter layer was designed to normalize multiple data formats (COCO / YOLO / VOC / image folders) into a standard structure, enabling any model to be paired with any dataset without code changes. This approach created a flexible, extensible system that fully supports “Plug and Play” integration of new models and datasets.

• Smart Resume-Implementation of an intelligent Resume mechanism that tracks run progress at batch-level resolution (DONE/PENDING) in the database, enabling execution to continue exactly from the batch where it stopped in case of a crash or intentional interruption — without restarting the process. This approach improves stability and saves significant time when working with large datasets and heavy models.

• Research : Development of an automatic Batch Size Optimizer for Vision model inference, incorporating latency/throughput/memory profiling, warmup runs, stability testing, and a combined exponential + binary search for fast and accurate batch-size tuning. The component was designed as a performance layer within the evaluation pipeline, providing clear insights into optimal resource utilization and enabling smarter, more efficient model execution.