A neural network’s performance can only be as good as the dataset.
Dataset development goes hand in hand with neural network development
Often, the most laborious task of developing a neural network is data collection and annotation.
Errors in annotation can cause inference errors which can be difficult to then "debug".
If you've tried to crowdsource data annotation and haven't been satisfied with the results, we can manage the job for you using our network of data annotation vendors, ensuring that you end up with high quality data.
If your neural network isn't performing well enough, the problem could be in the quality of your annotations or the quantity of data is too limited. We'll be able to diagnose the difference and create a plan of action.
Our dataset development in the field
A neural network which pre-labels data
We were challenged with creating a neural network to automate image labeling for a customer's dataset. This project required a very iterative, human-in-the-loop process. Our neural network pre-labeled the image and then the human corrected the image. We then used that corrected data to retrain our neural network, thus improving it's pre-labeling performance.