Curating a High-Quality Dataset for Model Training

An overview of ways to curate high quality datasets using Visual Layer.

Data curation is the process of turning raw data into high-quality, refined datasets and is crucial for developing effective and robust machine learning models.

It's not a trivial process and involves data collection, cleaning, annotation, exploration, and selection. Once ready, these datasets are then used to train, test, and validate machine learning models effectively.

Even after datasets are ready, the curation process is not complete and requires ongoing maintenance in the form of data updates, class balancing, preventing data drift, and removing obsolete data.

Visual Layer helps you accelerate and streamline this process at scale in a few simple steps:

  • Import Data: Create a Dataset with your visual data and annotations. Visual Layer then indexes your data, creates similarity clusters, and detects quality issues.
  • Remove Duplicates: Ensure there are no duplicate images to prevent skewed analysis.
  • Remove Noisy Data: Filter out images that are irrelevant or of poor quality, such as blurry, dark, or overly bright images.
  • Correct Annotations: Detect any missing or mislabeled annotations.
  • Select Training Data: Ensure you have enough representative and well-balanced data to train the model effectively.
  • Export Data: Once the right set is selected, export it and use it for model training.