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)
pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more. (by jacobgil)
| Transformer-Explainability | pytorch-grad-cam | |
|---|---|---|
| 1 | 5 | |
| 1,664 | 9,456 | |
| - | - | |
| 0.0 | 5.4 | |
| 3 months ago | about 2 months 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.
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.
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.
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[Project] Recent Class Activation Map Methods for CNNs and Vision Transformers
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
pytorch-grad-cam
Posts with mentions or reviews of pytorch-grad-cam.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-13.
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Exploring GradCam and More with FiftyOne
For the two examples we will be looking at, we will be using pytorch_grad_cam, an incredible open source package that makes working with GradCam very easy. There are excellent other tutorials to check out on the repo as well.
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Which layers are doing image segmentation on AutoEncoders/U-NET?
https://github.com/jacobgil/pytorch-grad-cam.
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[D] Algorithm for view prediction?
I know I would like to use grad-CAM https://github.com/jacobgil/pytorch-grad-cam
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[P] Adapting Class Activation Maps for Object Detection and Semantic Segmentation
https://github.com/jacobgil/pytorch-grad-cam is a project that has a comprehensive collection of Pixel Attribution Methods for PyTorch (like the package name grad-cam that was the original algorithm implemented).
- [Project] Recent Class Activation Map Methods for CNNs and Vision Transformers
What are some alternatives?
When comparing Transformer-Explainability and pytorch-grad-cam you can also consider the following projects:
shap - A game theoretic approach to explain the output of any machine learning model.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]