Popular GitHub Repositories for Explainability Metric

A game theoretic approach to explain the output of any machine learning model.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Fit interpretable models. Explain blackbox machine learning.

Torchreid: Deep learning person re-identification in PyTorch.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

? Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Metric depth estimation from a single image

A collection of research papers and software related to explainability in graph machine learning.

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Interpretability and explainability of data and machine learning models

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

:dart: Task-oriented embedding tuning for BERT, CLIP, etc.

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.

XAI - An eXplainability toolbox for machine learning

Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

Explainability for Vision Transformers

Universal Monocular Metric Depth Estimation

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Power Tools for AI Engineers With Deadlines