[D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?

This page summarizes the projects mentioned and recommended in the original post on /r/MachineLearning

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  • cockpit

    Cockpit: A Practical Debugging Tool for Training Deep Neural Networks (by f-dangel)

  • backpack

    BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • vivit

    [TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps

  • yellowbrick

    Visual analysis and diagnostic tools to facilitate machine learning model selection.

  • loss-landscape

    Code for visualizing the loss landscape of neural nets

  • Transformer-MM-Explainability

    [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.

  • captum

    Model interpretability and understanding for PyTorch

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • cleverhans

    An adversarial example library for constructing attacks, building defenses, and benchmarking both

  • TorchDrift

    Drift Detection for your PyTorch Models

  • shapash

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

  • uncertainty-toolbox

    Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

  • pytea

    PyTea: PyTorch Tensor shape error analyzer

  • explainerdashboard

    Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

  • deepchecks

    Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

  • AIX360

    Interpretability and explainability of data and machine learning models

  • delve

    PyTorch model training and layer saturation monitor (by delve-team)

  • WeightWatcher

    The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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