Manot is an insights management platform for computer vision model accuracy. It provides actionable insights in the form of images, pinpointing where, how and why the model is performing poorly.
Manot is model agnostic and does not take into account the model’s performance when evaluating which data samples the model will perform poorly on. Our algorithm takes the inference results on the model’s test data set, and makes predictions about the model’s blind spots using our proprietary 5 billion image data lake. Alternatively, it can generate insights from our advanced generative AI module.
Manot is easy to use, comprehensive, and designed for use by both product managers and engineering teams. By leaving no role behind, Manot ensures a continuous feedback loop that automates the data and model curation processes.
Simply log in to our platform In addition, Manot can provide on-premise solutions if needed.
Manot can provide insights from a variety of sources. The platform includes a 5 billion image data lake which insights can be provided from. In addition, users can upload their data, from which Manot can predict where the model will likely fail.
Manot takes the test set and the model's inference results as input. It employs an advanced scoring mechanism using embedding-based similarity analysis to identify potential false positives and false negatives.
Manot's generative AI module helps detect your model's false negatives and false postives!