Transformer-MM-Explainability VS explainerdashboard

Compare Transformer-MM-Explainability vs explainerdashboard 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 explainerdashboard
3 2
704 2,228
- -
0.0 7.8
8 months ago 27 days ago
Jupyter Notebook Python
MIT License MIT License
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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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.

explainerdashboard

Posts with mentions or reviews of explainerdashboard. 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 explainerdashboard 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.

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.

TorchDrift - Drift Detection for your PyTorch 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.

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

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

pytea - PyTea: PyTorch Tensor shape error analyzer

delve - PyTorch model training and layer saturation monitor

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