22 Labelbox Testimonials

“We needed a machine learning pipeline solution and Labelbox was it!”

"Labelbox facilitates the collaboration and management of multiple distributed labeling workforces, and the integration between our internal processes and the Labelbox platform is easy and works like a charm."

"Labelbox serves all of our training data needs. The team is very responsive and have provided exceptional support."

“Labelbox has become essential to our process. It is the beginning of every single deep learning exercise we do. The only thing that comes before Labelbox is recording the data.”

“Field management practices and problems impacting the field vary significantly per country, field, climatic zone and field zone. Regional experts are vital to achieving high quality in annotated data.”

“Labelbox is so easy to use. The documentation is accessible, and the labeling pipeline is straightforward. We just had to upload our data, customize the editor to our exact requirements, and go. We had actually budgeted a week to get it set up, but we were done in a day.”

“I found Labelbox and had a conversation with Brian. I was able to ask about the features I needed and enterprise level support.”

"Core to the enablement of AI based machine learning algorithms for our Intelligence Community and National Security partners is the need to accurately and cost-effectively label vast amounts of training data. Labelbox, offers our partners a state-of-the-art data annotation and data labeling platform for our IC partners to quickly and cost effectively label their AI training data."

"Labelbox has become the foundation of our training data infrastructure. Our data science teams create high quality labeled training data with our internal domain experts as well as external labeling services, all inside Labelbox. And, the support is exceptional!"

“There are many labeling tools out there but the Labelbox backend is the real differentiator. With dynamic queueing, our labelers are never out of work, which was a major upgrade compared to our old internal tools — both from a productivity and speed-to-production point of view.”

“Using your platform and Workforce service has been very easy and effortless. The quality of the labels, especially with regard to the care that was taken to make sure the boundaries of each zone were traced out as accurately as possible. This has really improved the results of our neural network beyond what we could have ever achieved.”

“We looked at several open source labeling solutions for our internal efforts and eventually selected Labelbox for its simplicity, ease of use, cost-effectiveness, and responsiveness of its development team. The choice was a good one and we will be using Labelbox again for our progressive and increasingly demanding AI and ML initiatives.”

“I really love the simplicity of the solution while still providing amazing functionality. The team is easy to get in touch with and are constantly working to improve Labelbox through user feedback and innovation. I highly recommend Labelbox for any size team that is working on a project that requires image segmentation or classification.”

“With the streamlined design of Labelbox, we are able to cut costs on labeling by as much as 50% while maintaining the highest quality in our training data, and get to training our models faster. With human-in-the-loop model-assisted labeling, we expect another huge reduction in time and costs to the labeling process. After a preliminary model is trained, we can run a loop to generate labels from our model’s inference, and feed those back into Labelbox, effectively cutting the labeling load of our labelers to that of reviewing for false positives. That allows us to increase our capabilities and model accuracies exponentially with respect to time for the amount of components and defects we can detect and classify.”

“Before using the Valohai and Labelbox platforms, we struggled with managing our training data creation infrastructure and manual experiment tracking. We’re now able to concentrate on model building and deployment, without sparing engineering effort and are able to speed up model training by over 10X.”