OmniXAI VS SegGradCAM

Compare OmniXAI vs SegGradCAM and see what are their differences.

SegGradCAM

SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (by kiraving)
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OmniXAI SegGradCAM
1 1
812 94
2.5% -
4.6 4.2
14 days ago 8 months ago
Jupyter Notebook Jupyter Notebook
BSD 3-clause "New" or "Revised" License BSD 3-clause "New" or "Revised" 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.

OmniXAI

Posts with mentions or reviews of OmniXAI. We have used some of these posts to build our list of alternatives and similar projects.

SegGradCAM

Posts with mentions or reviews of SegGradCAM. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing OmniXAI and SegGradCAM you can also consider the following projects:

DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.

super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

interpret - Fit interpretable models. Explain blackbox machine learning.

rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation

eli5 - A library for debugging/inspecting machine learning classifiers and explaining their predictions

STEGO - Unsupervised Semantic Segmentation by Distilling Feature Correspondences

shapash - ๐Ÿ”… Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

transformers-interpret - Model explainability that works seamlessly with ๐Ÿค— transformers. Explain your transformers model in just 2 lines of code.

DALEX - moDel Agnostic Language for Exploration and eXplanation

imodels - Interpretable ML package ๐Ÿ” for concise, transparent, and accurate predictive modeling (sklearn-compatible).

diffusers-interpret - Diffusers-Interpret ๐Ÿค—๐Ÿงจ๐Ÿ•ต๏ธโ€โ™€๏ธ: Model explainability for ๐Ÿค— Diffusers. Get explanations for your generated images.