Deepva landmark evaluation api
Build accurate AI models for Landmark Recognition
Building your own AI models for landmark recognition with DeepVA is an intuitive process and does not require any machine learning expertise. However, there may be instances where a user provides training data that limits the recognition performance of the AI model because certain quality standards in terms of resolution, relative size or position are not met. The Landmark Evaluation feature analyses entire data sets and provides feedback to the user on the quality of the training data they have provided.
Transforming data into value
Individual landmark identification
Feedback on data quality without AI expertise
Anyone can build their own AI models
Landmark evaluation use cases
Face evaluation checks the quality of the training data. This means that all training data is checked for usability in a separate AI model: For example, if people are wearing masks or the images are too blurry, this data is recognised and cleaned up. This improves the quality of the dataset and makes face recognition more accurate and reliable.