SegGradCAM
SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (by kiraving)
transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code. (by cdpierse)
| SegGradCAM | transformers-interpret | |
|---|---|---|
| 1 | 3 | |
| 94 | 1,213 | |
| - | - | |
| 4.2 | 2.9 | |
| 8 months ago | 8 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
SegGradCAM
Posts with mentions or reviews of SegGradCAM.
We have used some of these posts to build our list of alternatives
and similar projects.
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Negative gradients when calculating GradCAM heatmap
Code for https://arxiv.org/abs/2002.11434 found: https://github.com/kiraving/SegGradCAM
transformers-interpret
Posts with mentions or reviews of transformers-interpret.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[P] XAI Recipes for the HuggingFace 🤗 Image Classification Models
Very cool, I like seeing this. I also noticed the transformers interpret package has released support for an image classification explainer: https://github.com/cdpierse/transformers-interpret
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Using LIME to explain the predictions from a BERT model, it looks like "the", "and", "or" are "very important" features, and thus I don't think the model is learning anything interesting. Any tips?
You could look at the Transformers Interpret python library: https://github.com/cdpierse/transformers-interpret
- Show HN: Transformers Interpret – Explain and visualize Transformer models