Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
| Date | Stars |
|---|---|
| 2026-07-24 | 12925 |
| 2026-07-25 | 12927 |
| 2026-07-28 | 12936 |
| 2026-07-30 | 12936 |
| 2026-07-31 | 12942 |
| 2026-08-06 | 12942 |
Today
— stars today
This week
+6 stars this week
This month
— stars this month
Momentum
6.0
growth rate 0.05%/day
[](https://opensource.org/licenses/MIT)  [](https://pepy.tech/project/grad-cam) [](https://pepy.tech/project/grad-cam) # Advanced AI explainability for PyTorch `pip install grad-cam` Documentation with advanced tutorials: [https://jacobgil.github.io/pytorch-gradcam-book](https://jacobgil.github.io/pytorch-gradcam-book) This is a package with state of the art methods for Explainable AI for computer vision. This can be used for diagnosing model predictions, either in production or while developing models. The aim is also to serve as a benchmark of algorithms and metrics for research of new explainability methods. ⭐ Comprehensive collection of Pixel Attribution methods for Computer Vision. ⭐ Tested on many Common CNN Networks and Vision Transformers. ⭐ Advanced use cases: Works with Classification, Object Detection, Semantic Segmentation, Embedding-similarity and more. ⭐ Includes smoothing methods to make the CAMs look nice. ⭐ High performance: full support for batches of images in all methods. ⭐ Includes metrics for checking if you can trust the explanations, and tuning them for best performance.  | Method | What it does | |---------------------|-----------------------------------------------------------------------------------------------------------------------------| | GradCAM | Weight the 2D activations by the average gradient | | HiResCAM | Like GradCAM but element-wise multiply the activations with the gradients; provably guaranteed faithfulness for certain models | | GradCAMElementWise | Like GradCAM but element-wise multiply the activations with the gradients then apply a ReLU operation before summing | | GradCAM++ | Like GradCAM but uses second order gradients | | XGradCAM | Like GradCAM but scale the gradients by the normalized activations | | AblationCAM | Zero out activations and measure how the output drops (this repository includes a fast batched implementation) | | ScoreCAM | Perbutate the image by the scaled activations and measure how the output drops | | EigenCAM | Takes the first principle component of the 2D Activations (no class discrimination, but seems to give great results) | | EigenGradCAM | Like EigenCAM but with class discrimination: First principle component of Activations*Grad. Looks like GradCAM, but cleaner | | LayerCAM | Spatially weight the activations by positive gradients. Works better especially in lower layers | | FullGrad | Computes the gradients of the biases from all over the network, and then sums them | | Deep Feature Factorizations | Non Negative Matrix Factorization on the 2D activations | | KPCA-CAM | Like EigenCAM but with Kernel PCA instead of PCA | | F
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Jacob Gildenblat · Israel
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Oliver Hjermitslev · Alexandra Institute · Denmark
5
3
2
Mikyx-1
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Ziheng Zhang · The Ohio State University
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Nevermore
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Shreyas Ranganatha
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Yuta Fukasawa
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Zachary Mostowsky
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Yonghye Kwon · MarkAny · South Korea
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Akash A Desai · India
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3dab0ccdc00da2e6, topic:computer-vision, topic:object-detection, topic:image-classification
matched fp:3dab0ccdc00da2e6, topic:deep-learning, topic:pytorch
matched fp:3dab0ccdc00da2e6, topic:interpretability, topic:explainable-ai, readme:explainable