Are you tired of seeing your computer vision projects derailed by avoidable mistakes?

Discover how to overcome:

• Inconsistent annotations that skew your model’s performance.
• The lack of baseline models that makes it hard to measure progress.
• Data leakage that undermines your model’s reliability.

Takeaways:

• Practical tips to enhance model reliability and performance.
• Insights from real-world examples and case studies.

In this talk, I explored:

• A common process flaw that introduces bias you might not have considered
• The overlooked step that leaves you flying blind on performance
• A subtle error in data handling that can invalidate your entire model

Some key takeaways:

• A consistent annotation process is essential to ensure model reliability
• Starting with a baseline model enables you to measure progress and identify data issues sooner
• Proper data splitting to prevent data leakage ensures that your models will generalize to unseen data