#Explainable AI

Explore state-of-the-art methods for AI explainability in computer vision, including advanced Pixel Attribution and benchmarking tools. Supports diverse CNNs and Vision Transformers across use cases like classification and segmentation, with methods like GradCAM for enhanced visualization and interpretability metrics.

OmniXAI is an open-source library offering explainable AI for various data types like tabular, images, texts, and time-series. It supports traditional ML and deep learning models such as PyTorch and TensorFlow. The library includes diverse explanation methods to facilitate the integration of interpretability in applications. It also introduces a GPT explainer for better model understanding. Targeted at data scientists and ML professionals, OmniXAI provides a consistent interface and visualization dashboard to streamline decision-making and enhance insights.

The toolkit provides over 35 metrics across six categories to quantitatively evaluate neural network explanations. It supports models from PyTorch and TensorFlow, helping ensure explanations align with responsible AI standards. Suitable for various data types and methods, the toolkit fosters a reliable evaluation environment. Join its active development on Discord and explore features like EfficientMPRT and SmoothMPRT. Tutorials and installation guides are available for integration into AI projects.