Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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Updated
Jan 19, 2025 - Python
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
High-Performance Symbolic Regression in Python and Julia
Interpretability and explainability of data and machine learning models
moDel Agnostic Language for Exploration and eXplanation
Generate Diverse Counterfactual Explanations for any machine learning model.
XAI - An eXplainability toolbox for machine learning
ICCV 2023 Papers: Discover cutting-edge research from ICCV 2023, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ support visual intelligence development!
Explainability for Vision Transformers
Leave One Feature Out Importance
👋 Xplique is a Neural Networks Explainability Toolbox
Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Fast SHAP value computation for interpreting tree-based models
Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Interpretability for sequence generation models 🐛 🔍
Neural network visualization toolkit for tf.keras
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
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