How To Choose Computer Vision Software

LandingLens vs. Other Solutions

Ease of Use and Collaborate

There are generic AI platforms that have a large developer base and can integrate with other services. However often these platforms are built for Machine Learning (ML) professionals or data scientists and aren’t easy for others to use. In many applications, the people that need to use computer vision—including QA technicians, doctors, and farmers— don’t have an AI or ML background. We designed the LandingLens platform to be easy to use, even with no AI or ML experience. You don’t need to worry about things like hyperparameters and epochs—you can simply upload your images, label them, and LandingLens generates a model for you. This allows more users in your organization to adopt the platform and use their knowledge to build better models. 

Some platforms may specialize in labeling with advanced labeling features, but having an end-to-end platform simplifies development and deployment workflow, as well as continuous learning.

Performance and Accuracy

LandingLens is the top computer vision platform for various reasons, including detecting objects of interest more accurately than other systems, even in complex use cases.. We used two computer vision systems–LandingLens and another system–to create segmentation models based on the same dataset. We observed that LandingLens detected objects more accurately. The other system did not perform as well, and even decreased in performance as the number of classes (object types) increased. Furthermore, LandingLens reported fewer false positives, providing a 3x improvement over the other system.

In addition to providing advanced tools for better performance, a crucial aspect is the Data-Centric AI approach. Through our research, we consistently found that enhancing the quality of data has a greater influence on model performance than tweaking the model itself. LandingLens offers features such as defining defects and establishing consensus on labeling, quality checks for labels, and error analysis. These tools will guide users in improving their data, ultimately leading to superior performance.