RRepoGEO

REPOGEO REPORT · LITE

meta-pytorch/captum

Default branch master · commit 2cc52160 · scanned 5/25/2026, 2:56:32 AM

GitHub: 5,633 stars · 558 forks

AI VISIBILITY SCORE
82 /100
Healthy
Category recall
2 / 2
Avg rank #4.5 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface meta-pytorch/captum, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening statement to emphasize core differentiator

    Why:

    CURRENT
    Captum is a model interpretability and understanding library for PyTorch.
    COPY-PASTE FIX
    Captum is the unified, PyTorch-native library for model interpretability and understanding, offering a comprehensive suite of attribution and interpretability methods for PyTorch models.
  • mediumreadme#2
    Add a dedicated 'Key Features' section to README

    Why:

    COPY-PASTE FIX
    ### Key Features

- **Unified PyTorch-Native API**: A consistent interface for a wide range of attribution and interpretability methods, built specifically for PyTorch.
- **Comprehensive Method Suite**: Includes state-of-the-art algorithms such as Integrated Gradients, DeepLIFT, Grad-CAM, LIME, TCAV, TracIn, and more.
- **Seamless Integration**: Designed for quick integration with models built using domain-specific PyTorch libraries like `torchvision` and `torchtext`.
  • lowhomepage#3
    Review homepage for core differentiator emphasis

    Why:

    COPY-PASTE FIX
    Review the `captum.ai` homepage to ensure that Captum's core differentiator—its unified, PyTorch-native API for a comprehensive suite of attribution and interpretability methods—is prominently featured and easily discoverable.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
2 / 2
100% of queries surface meta-pytorch/captum
Avg rank
#4.5
Lower is better. #1 = top recommendation.
Share of voice
14%
Of all named tools, what % are you?
Top rival
SHAP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SHAP · recommended 2×
  2. LIME · recommended 2×
  3. ELI5 · recommended 2×
  4. Grad-CAM · recommended 1×
  5. Integrated Gradients · recommended 1×
  • CATEGORY QUERY
    How can I understand the decision-making process of my deep learning models?
    you: #5
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. Grad-CAM
    4. Integrated Gradients
    5. Captum ← you
    6. TensorFlow Explainability (TFX)
    7. ELI5
    Show full AI answer
  • CATEGORY QUERY
    What tools help determine feature importance and attribution for complex AI models?
    you: #4
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. ELI5
    4. Captum ← you
    5. InterpretML
    6. XAI
    7. Alibi Explain
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of meta-pytorch/captum?
    pass
    AI named meta-pytorch/captum explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts meta-pytorch/captum in production, what risks or prerequisites should they evaluate first?
    pass
    AI named meta-pytorch/captum explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo meta-pytorch/captum solve, and who is the primary audience?
    pass
    AI named meta-pytorch/captum explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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meta-pytorch/captum — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite