# PAIR-code/saliency

Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).

Repository: https://github.com/PAIR-code/saliency
Canonical: https://ross.abutalabs.com/products/saliency
Homepage: https://pair-code.github.io/saliency/
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: machine-learning, deep-learning, deep-neural-networks, tensorflow, convolutional-neural-networks, saliency-map, object-detection, image-recognition, ig-saliency, smoothgrad, saliency
Last push: 2024-03-20T19:44:13+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3372, "days_push": 896, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 996, forks 191 (observed 2026-09-03T02:15:07.024764+00:00)

## Summary
No AI-extracted summary yet.

## Member repositories
- PAIR-code/saliency (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:07.024764+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Data as of 2026-08-30T08:39:29.467469+00:00.