Transformer-MM-Explainability VS pytea

Compare Transformer-MM-Explainability vs pytea 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 pytea
3 3
704 310
- 0.3%
0.0 1.8
8 months ago about 2 years ago
Jupyter Notebook TypeScript
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.
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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.

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.

What are some alternatives?

When comparing Transformer-MM-Explainability and pytea 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.

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

TorchDrift - Drift Detection for your PyTorch Models

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

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.

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

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

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

delve - PyTorch model training and layer saturation monitor