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End-to-end Visual AI platform for training and deploying vision models

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Curate High Quality Datasets | Improve LandingLens Model Performance with Image Curation

LandingAI

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.

Weโ€™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:

ย โœ… Image quality and real-world relevance

ย โœ… Variations in lighting, shadows, and backgrounds

ย โœ… The impact of extreme edge cases and unrealistic scenarios

ย โœ… 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.

๐Ÿ”— 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

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