How do you label data for machine learning?

When data is labeled, it can be more easily inspected for errors. This can help to ensure that the model is trained on accurate data and that its predictions are reliable. By understanding how the model was trained, it is easier to understand how it makes its predictions. This can be important for ensuring that the model is making decisions in a way that is consistent with human understanding. Also when data is labeled by humans, there is always the potential for bias to be introduced. However, by carefully selecting and training the labelers, it is possible to reduce the bias in the model.

Also consider below points: What to Label: Clearly define each label. Unclear labels can cause issues in your machine learning models later on. Where to Get Data: Choose data that's both relevant and diverse. Also, make sure it complies with privacy laws like GDPR. Who Labels: If the subject is specialized, you might need experts. Consistency is key, so ensure everyone is on the same page. How to Label: You can use manual or automated methods, but a combination usually offers the best results for both speed and accuracy. Check Your Work: Use various techniques to validate that your labels are correct and reliable. Infrastructure: Don’t overlook the backend setup needs, especially if dealing with large-scale data sets.

I would like to add below points - Scalability: Often, projects start small but grow. Ensure your labeling approach is scalable to avoid rework and inconsistencies later on. Human Bias: Labelers come with their own set of biases. These can unintentionally influence the data, so checks are needed to mitigate this. Quality Assurance: Catching errors early is crucial. Implement automated checks or periodic audits to maintain label quality. Turnover: Labeling is sometimes seen as mundane. High turnover among labelers can affect consistency, so consider ways to keep them engaged. Resource Allocation: Labeling can be resource-heavy. Balance your manpower and tech stack to avoid bottlenecks.

While Deep Learning heavily relies on the variety of data, in reality, this latter is never as readily available as we like it to be. Speaking strictly of Computer Vision, DL models trained on small datasets are very prone to high biases and low variance. To prevent any sequels, there are a few tips and tricks to increase the volume of data. Let's cite a technique of Data Augmentation which consists in slightly tweaking the same photo for instance, to multiply the perspectives about the subjects or characters shown in the image, by cropping, blurring, contrasting, bird-view, sides etc. This way, we gain a few novel datasets easily. Other techniques include Generative Adversarial Networks (GANs), to be used within ethical policies.

Did you know that when we solve reCAPTCHAs about street elements we indirectly contribute to data labeling? Indeed, each time we solve those puzzles and click on *I am not a robot*, we help improve machine learning models in support of self-driving vehicles and world maps.

Data labeling and cleaning are crucial steps upstream of data preprocessing, while model training and deployment are downstream. As we say ''garbage in, garbage out'', then we need to be intentionally picky and rigorous in terms of data quality, and this, even if "to err is human". Computer Vision is mostly based on unsupervised learning thanks to the usage of ArtificiaI Neural Networks. Depending on the outcome, data labeling is necessary both at the beginning and in the end to improve the model's performance. The iterative process which involves a data labeler is what we call ''Human-In-the-loop'' (in opposition to ''Machine-in-the- loop'') which is at the basis of all hybrid / semi-supervised ML projects.