Transformer-Explainability VS T2T-ViT

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

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
Source Code
Suggest alternative
Edit details

T2T-ViT

ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet (by yitu-opensource)
vision-transformer t2t-transformer vit
Source Code
Suggest alternative
Edit details
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
www.influxdata.com
featured
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com
featured
Transformer-Explainability T2T-ViT
1 2
1,664 1,119
- 0.6%
0.0 0.0
3 months ago 6 months 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.
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.

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

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.

What are some alternatives?

When comparing Transformer-Explainability and T2T-ViT 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.

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

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

CeiT - Implementation of Convolutional enhanced image Transformer

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

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

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)

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