Encord

“Encord speeds up annotation while still allowing for strong quality control.”

“With the model assistance, we found a much higher increase in efficiency within Encord simply because most labels were produced by a trained model and did not require correction.”

““Using Encord has more than halved the time it takes us to provide feedback on imagery. What could have previously taken us 2 hours now takes approximately 30 minutes.””

““The fact that Encord was able to offer both an automatic labeling tool as well as a team of annotators that we could use was very attractive. We need that flexibility at this stage in our journey,””

“With Encord, myself and other team members can label the data ourselves. We have numerous projects that we want to achieve this year, and I think having an easy to use platform where we can all work together is going to help us move them forward quickly and successfully,” says Elmes.”

“We built separate pipelines for the three sets of images and labeled them accordingly. Then we trained and tested this small model. We got really good results, and it took more time to pull the images off the internet than it did to label them and build the model,” says Elmes.”

“After they helped us put the images together, we pressed a couple of buttons, and the platform made a model for us,” says Elmes. “It was so easy.”

“The platform was so intuitive. It provides a lot of different levels of control and team management. Everyone on the team could label and review images easily,” says Mathew Elmes, Director of Pollenize and The Far Out Thinking Company.”

“We could also do computation both on the cloud and locally on our deep learning machine. All those factors made it really advantageous for us, and I saw the opportunity to save a lot of time and costs by using Encord to speed up our model development.”

“"We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have. Specifically understanding how the video works behind the scenes: the encoding, the frame indexes and square pixel ratios."”

“The annotation platform is well designed for compatibility and interpretability. We were able to effectively align it with the current systems,”

“We plan on leveraging Encord to expand the range of use cases and include more medical conditions in our solutions… We are excited about our potential with Encord and look forward to seeing how we will evolve together in the future.”

“The division of tasks and clear role assignment ensures organized workflows and the ability for team members to consult certain cases with experts along the way.”

“The interpolation tool is a sophisticated, semi-automatic labeling tool that has proven to be a significant time saver for our team by reducing labeling time and improving precision.”

“Encord constantly impresses us with the ability to think outside the box,”

“Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted,”

“Encord helps us to offer a ‘best in class’ capability for counting trees. It has helped us to improve the computer vision algorithm that allows us to delineate individual tree tops. [We were able] to count the entire forest in two minutes as opposed to two weeks.”

“For our AI initiatives, rapid iteration is critical. Encord and our ML infrastructure allow us to  prototype learning tasks efficiently. The composability of Encord enables us to merge diverse data sources, facilitating extensive experimentation. With a well-integrated SDK, it's a matter of a few lines of code to achieve seamless integration and functionality.”