pytea VS Transformer-MM-Explainability

Compare pytea vs Transformer-MM-Explainability 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)
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pytea Transformer-MM-Explainability
3 3
310 709
0.3% -
1.8 0.0
about 2 years ago 8 months ago
TypeScript Jupyter Notebook
GNU General Public License v3.0 or later MIT License
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.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.

pytea

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

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.

What are some alternatives?

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

examples - A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

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

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

TorchDrift - Drift Detection for your PyTorch Models

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

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

WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

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

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

clip-italian - CLIP (Contrastive Languageā€“Image Pre-training) for Italian

delve - PyTorch model training and layer saturation monitor