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Intro and Logistics (Sara Beery)
[Slides]
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IDEs, Github, and remote workflow (Eric Orenstein)
[Slides]
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Intro to Computer Vision (Sara Beery)
[Video]
[Slides]
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Data Visualization, Data Splitting and Avoiding Data Poisoning (Julia Chae)
[Slides]
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Best Practices for a Computer Vision Codebase (Björn Lütjens)
[Slides]
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Working with Open-source CV Codebases (Sam Lapp)
[Slides]
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Evaluation Metrics (Shir Bar)
[Slides]
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Probing your Model’s Performance: Offline Evaluation & Analysis (Sam Lapp)
[Slides]
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What's next? Rules of Thumb to Improve Results (Mélisande Teng)
[Slides]
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Squeezing Your Data: Data Augmentation and Self-Supervised Learning (Björn Lütjens)
[Slides]
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Fair Comparisons and Ablation Studies - Understanding What is Important (Shir Bar)
[Slides]
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Efficient Models and Speed vs Accuracy (Peter van Lunteren)
[Slides]
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What Do I Do with my (Imperfect) Model? (Sam Lapp)
[Slides]