REPOGEO REPORT · LITE
caiyuanhao1998/MST
Default branch main · commit 60a11291 · scanned 5/21/2026, 1:02:41 AM
GitHub: 1,115 stars · 88 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 caiyuanhao1998/MST, 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#1Reposition README H1 to explicitly clarify the project's domain
Why:
CURRENT# A Toolbox for Spectral Compressive Imaging
COPY-PASTE FIX# `caiyuanhao1998/MST`: A Toolbox for Spectral Compressive Imaging Reconstruction (NOT Minimum Spanning Tree)
- mediumhomepage#2Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIXhttps://[your-project-homepage-url]
- mediumreadme#3Expand README introduction with a clear value proposition and algorithm list
Why:
CURRENT#### Introduction This is a baseline and toolbox for spectral compressive imaging reconstruction. This repo supports **over 15** algorithms. Our method MST++ won the NTIRE 2022 Challenge on spectral recovery from RGB images. If you find this repo useful, please give it a star ⭐ and consider citing our paper in your research. Thank you.
COPY-PASTE FIX#### Introduction This repository provides a comprehensive baseline and toolbox for **Spectral Compressive Imaging (SCI) reconstruction**. It supports **over 15 state-of-the-art algorithms**, including MST (Mask-guided Spectral-wise Transformer), CST, DAUHST, BiSCI, HDNet, and MST++. Our methods have achieved top results, notably MST++ winning the NTIRE 2022 Challenge on spectral recovery from RGB images. If you find this repo useful, please give it a star ⭐ and consider citing our paper in your research. Thank you.
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.
- TensorFlow/Keras · recommended 1×
- PyTorch · recommended 1×
- MATLAB · recommended 1×
- scikit-learn · recommended 1×
- OpenCV · recommended 1×
- CATEGORY QUERYWhat tools are available for efficient hyperspectral image reconstruction from compressed data?you: not recommendedAI recommended (in order):
- TensorFlow/Keras
- PyTorch
- MATLAB
- scikit-learn
- OpenCV
- HyperSpy
AI recommended 6 alternatives but never named caiyuanhao1998/MST. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking a library for spectral image restoration using transformer models or binarized networks.you: not recommendedAI recommended (in order):
- Hugging Face Transformers (huggingface/transformers)
- PyTorch Image Models (timm) (rwightman/pytorch-image-models)
- TensorFlow Models (Official Models) (tensorflow/models)
- MMEditing (OpenMMLab) (open-mmlab/mmediting)
- Brevitas (Xilinx/brevitas)
- KerasCV (keras-team/keras-cv)
AI recommended 6 alternatives but never named caiyuanhao1998/MST. This is the gap to close.
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 caiyuanhao1998/MST?passAI did not name caiyuanhao1998/MST — 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 caiyuanhao1998/MST in production, what risks or prerequisites should they evaluate first?passAI named caiyuanhao1998/MST 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 caiyuanhao1998/MST solve, and who is the primary audience?passAI did not name caiyuanhao1998/MST — 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
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caiyuanhao1998/MST — 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