This repository provides state-of-the-art explainability methods for computer vision models in PyTorch. It implements various pixel attribution techniques like Grad-CAM and Score-CAM for diagnosing model predictions. The library supports CNNs and Vision Transformers across tasks including classification, object detection, semantic segmentation, and embedding similarity. It also includes smoothing methods and trust metrics for evaluating explanation quality.