Tehila G.

Responsibilities:

• Architected the overall evaluation flow, separating runners, dataset adapters, metric computation, and persistence into clear layers to keep the system scalable and easy to extend with new models and benchmarks.

• Set up a Docker-based environment that packages backend services, the database, and supporting tools into a reproducible, one-command setup for local development and demos.

• Implemented the runner layer that orchestrates the full evaluation flow – loading models, running inference, aggregating results, and computing detection/classification/segmentation metrics across benchmarks.

• Managed and authored PostgreSQL schema design, data ingestion, and Alembic migrations, using MinIO for persistent storage.

• Implemented a robust model-to-benchmark class-ID mapping layer to align dataset labels with canonical classes and ensure consistent evaluation across datasets and experiments.

• Fine-tuned and compared YOLO-based detection models on project datasets using transfer-learning techniques (freezing the backbone, training detection heads, and then gradually unfreezing layers), and evaluated their performance and trade-offs with PyQt-based experiment visualizations.

• Research: Conducted research and delivered a presentation on large-language-model evaluation metrics, comparing different approaches and their implications for real-world use.