REPOGEO REPORT · LITE
fbcotter/pytorch_wavelets
Default branch master · commit 9a0c507f · scanned 5/14/2026, 4:17:13 AM
GitHub: 1,169 stars · 159 forks
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 fbcotter/pytorch_wavelets, 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.
- highreadme#1Emphasize 2D DWT efficiency for image processing in README intro
Why:
CURRENTThis package provides support for computing the 2D discrete wavelet and the 2d dual-tree complex wavelet transforms, their inverses, and passing gradients through both using pytorch.
COPY-PASTE FIXThis PyTorch package provides highly efficient implementations of 2D Discrete Wavelet Transforms (DWT) and Dual-Tree Complex Wavelet Transforms (DTCWT), their inverses, and gradient support. It is specifically optimized for image processing and computer vision tasks, handling batches of multichannel images seamlessly.
- mediumlicense#2Add explicit license statement to README
Why:
COPY-PASTE FIX## License This project is licensed under [MAINTAINER: Specify License Name(s) here, e.g., 'a custom license combining MIT and Apache-2.0 terms']. Please refer to the `LICENSE` file for full details.
- lowhomepage#3Add homepage URL to repository About section
Why:
COPY-PASTE FIXhttp://pytorch-wavelets.readthedocs.io/
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.
- PyWavelets (PyWt) · recommended 1×
- Kornia · recommended 1×
- Torch-Wavelets · recommended 1×
- Custom PyTorch Implementation · recommended 1×
- kymatio · recommended 1×
- CATEGORY QUERYHow to perform 2D discrete wavelet transforms efficiently in PyTorch for image processing?you: not recommendedAI recommended (in order):
- PyWavelets (PyWt)
- Kornia
- Torch-Wavelets
- Custom PyTorch Implementation
AI recommended 4 alternatives but never named fbcotter/pytorch_wavelets. This is the gap to close.
Show full AI answer
- CATEGORY QUERYNeed a fast PyTorch library for dual-tree complex wavelet transforms on image batches.you: #1AI recommended (in order):
- pytorch_wavelets ← you
- kymatio
- dtcwt
- TorchWavelets
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 fbcotter/pytorch_wavelets?passAI did not name fbcotter/pytorch_wavelets — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts fbcotter/pytorch_wavelets in production, what risks or prerequisites should they evaluate first?passAI named fbcotter/pytorch_wavelets 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 fbcotter/pytorch_wavelets solve, and who is the primary audience?passAI did not name fbcotter/pytorch_wavelets — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of fbcotter/pytorch_wavelets. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
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fbcotter/pytorch_wavelets — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite