Transformer-MM-Explainability VS TorchDrift

Compare Transformer-MM-Explainability vs TorchDrift 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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Transformer-MM-Explainability TorchDrift
3 1
704 302
- 0.0%
0.0 0.0
8 months ago over 1 year ago
Jupyter Notebook Jupyter Notebook
MIT License GNU General Public License v3.0 or later
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.

TorchDrift

Posts with mentions or reviews of TorchDrift. 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 TorchDrift 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.

cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks

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

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

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

loss-landscape - Code for visualizing the loss landscape of neural nets

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

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

pytea - PyTea: PyTorch Tensor shape error analyzer

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