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- Intro and Logistics (Sara Beery) [Slides]
- IDEs, Github, and Remote Workflow (Eric Orenstein) [Slides]
- Intro to CV (Sara Beery) [Slides]
- Data Visualization, Splitting, and Avoiding Overfitting (Michael Hobley) [Slides]
- Interactive exercise: How to work effectively with LLM coding assistants (Sam Lapp, remote)
- Interactive exercise: Working with Open-source CV Codebases – Choosing a Baseline Model and Custom Data Loading (Michael Hobley and Louis Freeland-Haynes)
- Deciding on Configurations, Launching, Monitoring, Checkpointing, and Keeping Runs Organized (Brian Geuther) [Slides]
- Evaluation Metrics (Shir Bar) [Slides]
- Probing your Model's Performance (Jose F. Ruiz-Munoz) [Slides]
- What's next? Rules of thumb to improve results (Alba Márquez-Rodríguez) [Slides]
- Fair Comparisons and Ablation Studies – Understanding What is Important (Shir Bar) [Slides]
- Efficient Models and Speed vs Accuracy (Octave Mariotti) [Slides]
- Interactive exercise: How to read CV papers (Sara Beery)
- What Do I Do With My (Imperfect) Model? (Louis Freeland-Haynes)