The success of a deep learning model for vision tasks starts with the right dataset. In this video, we explore how to curate an optimal, high-quality dataset that aligns with your specific machine learning task.
We’ll walk through real-world examples of what to include and what to discard when preparing training data. Using a dataset of hand-drawn electrical circuit diagrams, we analyze key factors like:
✅ Image quality and real-world relevance
✅ Variations in lighting, shadows, and backgrounds
✅ The impact of extreme edge cases and unrealistic scenarios
✅ How different drafters influence dataset diversity
By the end of this video, you’ll have a clear strategy for selecting images that improve model performance while avoiding common dataset pitfalls.
🔗 Check out our support page for more best practices on curating high-quality datasets:
https://support.landing.ai/docs/curate-high-quality-datasets
Data source: https://tc11.cvc.uab.es/datasets/CGHD_1
