RRepoGEO

REPOGEO REPORT · LITE

NVlabs/RADIO

Default branch main · commit fbd19ec1 · scanned 5/22/2026, 11:22:53 AM

GitHub: 1,816 stars · 67 forks

Scan history for this repo

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.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 NVlabs/RADIO, 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
  • hightopics#1
    Add explicit topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    pytorch, computer-vision, foundation-model, vision-model, domain-generalization, feature-learning, deep-learning, nvidia-research
  • highreadme#2
    Clarify the project's domain and what 'RADIO' stands for immediately after the H1

    Why:

    CURRENT
    Official PyTorch implementation of [CVPR 2025] RADIOv2.5: Improved Baselines for Agglomerative Vision Foundation Models
    COPY-PASTE FIX
    AM-RADIO (Agglomerative Vision Foundation Model - Reduce All Domains Into One) is a PyTorch-based computer vision project focused on developing foundation models that generalize effectively across diverse image domains. This repository provides the official implementation for our CVPR 2024 and 2025 papers.
  • mediumreadme#3
    Explicitly mention 'feature sharpening' and 'generalization' in the README

    Why:

    CURRENT
    Check out our preprints: PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation and FeatSharp: Your Vision Model Features, Sharper.
    COPY-PASTE FIX
    Check out our preprints: PHI-S: Distribution Balancing for Label-Free Multi-Teacher Distillation and FeatSharp: Your Vision Model Features, Sharper. AM-RADIO and its related works, such as FeatSharp, explore advanced techniques for sharpening vision model features to achieve better generalization across various visual tasks and datasets.

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
0 / 2
0% of queries surface NVlabs/RADIO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ResNet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ResNet · recommended 2×
  2. EfficientNet · recommended 2×
  3. Vision Transformer (ViT) · recommended 1×
  4. DeiT · recommended 1×
  5. MAE · recommended 1×
  • CATEGORY QUERY
    Seeking a PyTorch vision model that generalizes effectively across diverse image domains.
    you: not recommended
    AI recommended (in order):
    1. Vision Transformer (ViT)
    2. DeiT
    3. MAE
    4. ConvNeXt
    5. Swin Transformer
    6. ResNet
    7. EfficientNet

    AI recommended 7 alternatives but never named NVlabs/RADIO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques for sharpening vision model features for better generalization?
    you: not recommended
    AI recommended (in order):
    1. DINO (facebookresearch/dino)
    2. DINOv2 (facebookresearch/dinov2)
    3. MAE (Masked Autoencoders) (facebookresearch/mae)
    4. SimCLR (google-research/simclr)
    5. MoCo (facebookresearch/moco)
    6. BYOL (Bootstrap Your Own Latent) (deepmind/deepmind-research/tree/master/byol)
    7. ViT (Vision Transformer)
    8. Hugging Face Transformers (huggingface/transformers)
    9. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
    10. Swin Transformer (microsoft/Swin-Transformer)
    11. ConvNeXt (facebookresearch/ConvNeXt)
    12. DeiT (Data-efficient Image Transformers) (facebookresearch/deit)
    13. Albumentations (albumentations-team/albumentations)
    14. RandAugment (tensorflow/tpu/tree/master/models/official/efficientnet/randaugment)
    15. AutoAugment (tensorflow/tpu/tree/master/models/official/efficientnet/autoaugment)
    16. CutMix (clovaai/CutMix-PyTorch)
    17. Mixup (facebookresearch/mixup-cifar10)
    18. PyTorch (pytorch/pytorch)
    19. TensorFlow (tensorflow/tensorflow)
    20. ResNet
    21. EfficientNet
    22. Scikit-learn (scikit-learn/scikit-learn)

    AI recommended 22 alternatives but never named NVlabs/RADIO. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 NVlabs/RADIO?
    pass
    AI named NVlabs/RADIO explicitly

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

  • If a team adopts NVlabs/RADIO in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVlabs/RADIO 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 NVlabs/RADIO solve, and who is the primary audience?
    pass
    AI named NVlabs/RADIO explicitly

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

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NVlabs/RADIO — 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
NVlabs/RADIO — RepoGEO report