Captum is a powerful open-source library specifically developed to enhance the interpretability of PyTorch models. It offers a comprehensive suite of tools that allow developers and researchers to analyze and explain the decision-making processes of machine learning models in detail. The library supports a wide range of interpretability techniques, including attribution methods, feature importance analysis, and layer-wise relevance propagation. These features help users identify the key inputs and features that influence model predictions, making it easier to debug, validate, and optimize models. Captum is particularly useful in industries where model transparency is critical, such as healthcare, finance, and autonomous systems. The library is designed with a user-friendly API that seamlessly integrates with PyTorch, ensuring ease of use. Additionally, Captum comes with extensive documentation and tutorials to help users quickly master its functionalities. Whether you are a seasoned machine learning professional or just starting out, Captum provides the tools you need to make your models more interpretable and trustworthy.