RRepoGEO

REPOGEO REPORT · LITE

yzhuoning/Awesome-CLIP

Default branch main · commit 654df44d · scanned 5/15/2026, 7:22:52 PM

GitHub: 1,231 stars · 59 forks

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 yzhuoning/Awesome-CLIP, 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
    Clarify repo type as 'awesome list' in README's opening sentence

    Why:

    CURRENT
    This repo collects the research resources based on CLIP (Contrastive Language-Image Pre-Training) proposed by OpenAI.
    COPY-PASTE FIX
    This repository is an **awesome list** that collects research resources based on CLIP (Contrastive Language-Image Pre-Training) proposed by OpenAI.
  • hightopics#2
    Add 'awesome-list' topic

    Why:

    CURRENT
    clip, contrastive-learning, pre-training
    COPY-PASTE FIX
    clip, contrastive-learning, pre-training, awesome-list
  • highlicense#3
    Add a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, for example, using the MIT License template, to clearly state the terms of use for the collected resources.

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 yzhuoning/Awesome-CLIP
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/diffusers · recommended 1×
  3. huggingface/datasets · recommended 1×
  4. Lightning-AI/pytorch-lightning · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    What tools are available for developing contrastive image-text embeddings?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Diffusers (huggingface/diffusers)
    3. Hugging Face Datasets (huggingface/datasets)
    4. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    5. PyTorch (pytorch/pytorch)
    6. OpenCLIP (mlfoundations/open_clip)
    7. OpenAI's CLIP model (openai/CLIP)
    8. TensorFlow (tensorflow/tensorflow)
    9. Keras (keras-team/keras)
    10. TensorFlow Hub (tensorflow/hub)
    11. MMDetection (open-mmlab/mmdetection)
    12. MMEngine (open-mmlab/mmengine)
    13. OpenMMLab
    14. JAX (google/jax)
    15. Flax (google/flax)

    AI recommended 15 alternatives but never named yzhuoning/Awesome-CLIP. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform text-guided image manipulation with deep learning models?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. ControlNet
    3. InstructPix2Pix
    4. DALL-E 2
    5. GLIDE
    6. StyleCLIP
    7. CompVis/latent-diffusion (CompVis/latent-diffusion)

    AI recommended 7 alternatives but never named yzhuoning/Awesome-CLIP. 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 yzhuoning/Awesome-CLIP?
    pass
    AI named yzhuoning/Awesome-CLIP explicitly

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

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

    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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MARKDOWN (README)
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yzhuoning/Awesome-CLIP — 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