Scalable Data Labeling: It’s all about your strategy
Today, one of the biggest time constraints around producing domain specific AI models is labeling the data. At CrowdAI, we believe that this does not have to derail your AI initiatives!
When creating a computer vision model to detect an object of interest, you typically need to train the model with lots of examples of that object. Subject matter experts just don’t have the bandwidth to label all that media, especially as domains and ontology continue to shift. This is where strategy comes into play; pre-processing the media, using effective annotation tools, being selective about your model architecture, and leveraging CrowdAI’s no-code platform can cut labeling time down from weeks to hours.
On Jan. 26th, at the Strategy and Warfare Center Symposium in Colorado, Chief Digital and AI Officer Craig Martell said, “If we’re going to beat China, and we have to beat China in AI, we have to find a way to label at scale. Because if we don’t label at scale, we’re not going to win.”
CrowdAI’s team of annotation ontology experts have experience creating timesaving strategies for annotation, putting scaleable labeling at your fingertips.
Strategy Tip 1:Limit your scope
Starting with the data itself, CrowdAI pre-processes data to reduce the time it takes to label. For example, geospatial data can be especially large which takes data labelers an extensive amount of time to zoom and pan across the frame to find the objects of interest. CrowdAI has a built-in feature which will tile the original media automatically and turn one large image into dozens, making it easier to scan the image in a fraction of the time and identify the object of interest.
Strategy Tip 2:Choose an annotation interface that expedites the work
Using an annotation tool that has been created with customized model building in mind, naturally accelerates the process of labeling. At CrowdAI, we have built just that. We have a team dedicated to labeling and their feedback is implemented directly into our product roadmap, making our labeling interface user friendly and customized to the needs of fast and efficient labeling.
Within our annotation interface, the tools we have built advance your labeling strategy; working with you, not against you. To highlight a few, our editing tool allows you to cut and paste dozens of annotations from image to image and across frames of video, speeding up the process of moving through data. CrowdAI’s video interpolation tool allows users to label only two frames and watch as the platform fills in the rest, making tracking a moving object quick and simple. Our cut tool allows you to trim labels as opposed to starting over, saving you editing time and headaches.
Strategy Tip 3: Optimize labeling through choosing the best model architecture
Leverage our state of the art model architecture, Keypoints, in conjunction with our few shot learning technique to speed up annotation efforts. With this method, you can now add a new type of object of interest to our model library for automated identification -- in a matter of minutes. Our model's accuracy, built with this approach, speaks for itself. Operating with an accuracy of 91%, we have found a method to strategically label at scale.
Strategy Tip 4: Iterate on models built with small amounts of data
CrowdAI’s no-code platform enables users to quickly iterate on a model to further improve its accuracy rather than spending time annotating large amounts of data. We have spent years perfecting the tools which allow for model training on small datasets, and through the platform we put that power directly in your hands. Users can rapidly train a model using 200 images to detect objects of interest and then use that initial model endlessly to pre-annotate new data. We are eliminating the time users spend on remedial annotations and letting the platform do the work by refining the model with each iteration. Need to update your model for a new environment? Forget about annotating thousands of images, iterate on a small dataset and watch as the model evolves.
Iterating models on small batches of data, becomes increasingly important when creating scalable models. As foundation models continue to take center stage, strategic annotation will be increasingly more important for fine-tuning domain specific models. Users will spend less time annotating to find commonly defined and identified objects, and instead annotation time will be dedicated to focusing on high order tasks or more specific ontology.