Transformer-MM-Explainability VS yellowbrick

Compare Transformer-MM-Explainability vs yellowbrick and see what are their differences.

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. (by hila-chefer)
Transformers Transformer vqa detr Visualization explainability explainable-ai Interpretability lxmert visualbert clip
Source Code
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yellowbrick

Visual analysis and diagnostic tools to facilitate machine learning model selection. (by DistrictDataLabs)
Machine Learning visual-analysis model-selection Visualization scikit-learn Visualizer Matplotlib Python estimator Anaconda
Source Code
scikit-yb.org
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Transformer-MM-Explainability yellowbrick
3 2
709 4,198
- 0.3%
0.0 2.8
8 months ago 9 months ago
Jupyter Notebook Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Transformer-MM-Explainability

Posts with mentions or reviews of Transformer-MM-Explainability. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

yellowbrick

Posts with mentions or reviews of yellowbrick. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

What are some alternatives?

When comparing Transformer-MM-Explainability and yellowbrick you can also consider the following projects:

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

kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

TorchDrift - Drift Detection for your PyTorch Models

Anaconda - Anaconda turns your Sublime Text 3 in a full featured Python development IDE including autocompletion, code linting, IDE features, autopep8 formating, McCabe complexity checker Vagrant and Docker support for Sublime Text 3 using Jedi, PyFlakes, pep8, MyPy, PyLint, pep257 and McCabe that will never freeze your Sublime Text 3

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

itermplot - An awesome iTerm2 backend for Matplotlib, so you can plot directly in your terminal.

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

seaborn-image - High-level API for attractive and descriptive image visualization in Python

clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian

fpdf2 - Simple PDF generation for Python

pytea - PyTea: PyTorch Tensor shape error analyzer

scikit-survival - Survival analysis built on top of scikit-learn

Transformer-MM-Explainability vs pytorch-grad-cam yellowbrick vs kmodes Transformer-MM-Explainability vs TorchDrift yellowbrick vs Anaconda Transformer-MM-Explainability vs explainerdashboard yellowbrick vs itermplot Transformer-MM-Explainability vs shap yellowbrick vs seaborn-image Transformer-MM-Explainability vs clip-italian yellowbrick vs fpdf2 Transformer-MM-Explainability vs pytea yellowbrick vs scikit-survival