Manot is an observability platform designed to help improve computer vision models by providing actionable insights and mitigating biases.
Computer vision models are algorithms or neural networks designed to analyze and interpret visual data, such as images and videos, and can be used for a variety of applications, such as surveillance, facial recognition, and autonomous vehicles.
Bias in the computer vision model refers to the presence of systematic errors or inaccuracies in the model's predictions, which can result in unfair or discriminatory outcomes.
Bias can arise from various factors, such as the data used to train the model, the algorithms and techniques used to develop the model, and the context in which the model is used.
With its advanced scoring system, Manot takes your training data and real-world data and identifies the areas where your model underperforms, detects hidden biases, and suggests ready data samples that will contribute the most to rebalancing the training dataset.
Computer vision model ethics refers to the ethical considerations and principles that should be taken into account when developing and deploying computer vision models.
