Improve your ML models by curating your vision data
Find and remove redundancy and bias introduced by the data collection process to reduce overfitting and improve ML model generalization.
Save money on your data related costs by removing redundancies
Reduce overfitting and improve generalization by diversifying your dataset
Understand your data within minutes after collection and before any data labeling.
We use self-supervised learning combined active-learning to accelerate your data preparation pipeline.
Most companies only use between 0.1% and 10% of their data for machine learning. Use our state-of-the-art methods to select the most relevant samples. Let WhatToLabel handle the selection of the data for you while you focus on the training process.
Keep track of the data your team is working on. Our algorithms help you only adding relevant data to the existing pool. We only store non-sensitive meta-information on our servers so you don't have to worry about transfer costs or privacy issues.
Use our deep data analytics framework to analyze your raw datasets. Get insights about the distribution, diversity, and other key metrics. Find dataset bias before training and evaluating your model.
Make your vehicle autonomous for the street, sea, or air.
Shipping, Logistics, Airline, Defense & Military
Detect defects in infrastructure, manufactured products, or find infected plants.
Railways & Roads, Infrastructure, Manufacturing, Agriculture, Surveillance & Security
Find abnormalities in medical images such as X-rays, MRIs, microscope & medical scans.
Health/Life Science, Biotechnology, and Digital Diagnostics/Pathology
Automatize check-out and shoplifting detection. Improve your advertising and vision-based products.
E-commerce, Retail, Platforms, Advertising & Marketing
We have the right solution for every amount of data. Use our webapp together with our PIP package to analyze and filter your first dataset within minutes.
You can try out our limited free version with no payment required!
Learn how AI Retailer Systems was able to reduce the data required to train an object detection model by 85% with almost no loss in accuracy thanks to WhatToLabel.
"I was truly amazed once we received the results of WhatToLabel. We knew we had a lot of similar images due to our video feed but the results showed us how we can work more efficiently by selecting the right data"
Alejandro Garcia, CEO
"After training a model on the filtered data suggested by WhatToLabel, I saw a dramatic increase in performance on our key metrics. Part of this is certainly because this was the first time we trained a model on any data that we've collected, but I'm fairly certain that performance would not have been as good if we had chosen what data to label at random."
Angelo Stekardis, Computer Vision Lead
"WhatToLabel helped us understand more about our own data gathering process. Through their service, we were able to see, that a lot of data being collected was not meaningful enough for training an accurate model. This led us to change the way we gathered data and allowed us to ultimately create a much more information dense and higher quality dataset overall. Needless to say, the performance of our final model was greatly improved."
Nasib Adriano Naimi, Autonomy and Robotics Engineer
In few-shot learning, we train a model using only a few labeled examples. Learn how to train your classifier using transfer learning and a novel framework for sample selection.
Lots of interesting Deep learning applications rely on the use of complex architectures fueled by large datasets. However, when doing so, one ends up with lots of redundancies within the dataset.
This article provides a summary of popular optimizers used in computer vision, natural language processing, and machine learning in general.