T2T-ViT VS Transformer-Explainability

Compare T2T-ViT vs Transformer-Explainability and see what are their differences.

T2T-ViT

ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet (by yitu-opensource)
vision-transformer t2t-transformer vit
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Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks. (by hila-chefer)
Deep Learning vision-transformer bert-model Bert explainability transformer-interpretability perturbation attention-visualization visualize-classifications vit attention-matrix cvpr2021
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T2T-ViT Transformer-Explainability
2 1
1,120 1,664
0.7% -
0.0 0.0
6 months ago 3 months ago
Jupyter Notebook Jupyter Notebook
GNU General Public License v3.0 or later MIT License
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T2T-ViT

Posts with mentions or reviews of T2T-ViT. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-12.

Transformer-Explainability

Posts with mentions or reviews of Transformer-Explainability. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-25.
  • [Project] Recent Class Activation Map Methods for CNNs and Vision Transformers
    2 projects | /r/MachineLearning | 25 Apr 2021
    Not exactly the same but since you mentioned using ViT's attention outputs as a 2D feature map for the CAM you can consider this paper (Transformer Interpretability Beyond Attention Visualization) where they study the question of how to choose/mix the attention scores in a way that can be visualized (so similar to the CAMs). Maybe it can lead to better results. https://arxiv.org/abs/2012.09838 https://github.com/hila-chefer/Transformer-Explainability

What are some alternatives?

When comparing T2T-ViT and Transformer-Explainability you can also consider the following projects:

HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

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

CeiT - Implementation of Convolutional enhanced image Transformer

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

Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.

multi-label-sentiment-classifier - How to build a multi-label sentiment classifiers with Tez and PyTorch

AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]

tf-metal-experiments - TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

deep-text-recognition-benchmark - PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficient Scene Text Recognition (ViTSTR)

T2T-ViT vs HugsVision Transformer-Explainability vs pytorch-grad-cam T2T-ViT vs CeiT Transformer-Explainability vs shap T2T-ViT vs Transformers-Tutorials Transformer-Explainability vs multi-label-sentiment-classifier T2T-ViT vs AutoML Transformer-Explainability vs HugsVision Transformer-Explainability vs tf-metal-experiments Transformer-Explainability vs deep-text-recognition-benchmark