Dataset of 150k+ stool images and not sure how to fully use it [D]
Our take
You have a substantial dataset of over 150,000 stool images, yet your current manual verification process may limit efficiency as you scale. While training on a meticulously curated subset of 5,000 images lays a strong foundation, the ongoing manual review could become a bottleneck. To enhance your workflow, consider incorporating semi-automated annotation tools or leveraging active learning techniques that prioritize the most informative samples.
The article delves into the practical challenges of managing a substantial dataset of 150,000 stool images and the nuanced strategies people employ to ensure high-quality annotations. At its core, the discussion highlights a common workflow where teams manually verify each image before feeding it into a machine learning model. This approach, while thorough, raises questions about efficiency and scalability. Many experts acknowledge that traditional methods like human review are essential, especially in domains where label accuracy directly impacts model performance.
Understanding this, the piece explores whether this manual process aligns with best practices in ML development. It underscores the importance of maintaining consistency in annotations, even as datasets grow. The author questions whether this method is sustainable or if there are smarter ways to incorporate quality checks without sacrificing speed. This inquiry is particularly relevant for professionals who recognize the fine balance between precision and productivity.
What stands out is the emphasis on iterative training and the need for continuous refinement. As models evolve, the ability to adapt annotation strategies becomes crucial. The article doesn’t dismiss manual work outright but encourages a thoughtful approach—one that values both human expertise and the potential of automated systems. It’s a reminder that while technology advances, the foundation of quality often lies in careful, intentional processes.
The conversation also touches on the broader implications for data scientists and developers. By reflecting on these challenges, teams can better design workflows that prioritize accuracy without losing sight of efficiency. Ultimately, the value lies not just in the numbers but in the intent behind each step, ensuring that the final models truly serve their intended purpose. This piece invites readers to consider how their own projects might benefit from similar reflections.
I have a dataset of around 150k stool images, and I’m trying to better understand the “right” way to use it for training a computer vision model.
Right now, our process is pretty manual. We initially trained on about 5k images that were individually verified by a human. For every image, we checked/corrected the Bristol type, consistency, color, mucus/blood indicators, etc. Then we trained the model on those verified annotations.
As we continue training, we keep doing the same thing: manually reviewing and correcting images before feeding them back into the model.
My question is basically: does this workflow make sense from an ML perspective? Is this how people normally approach building a solid vision dataset/model, especially in a domain where annotation quality matters a lot? Or is there a smarter/more scalable approach people usually move toward once they have a large dataset?
I’m mainly trying to understand best practices around dataset quality, human verification, iterative training, and scaling annotation without introducing bad labels.
[link] [comments]
Read on the original site
Open the publisher's page for the full experience