RRepoGEO

REPOGEO REPORT · LITE

megvii-research/mdistiller

Default branch master · commit a08d46f1 · scanned 6/11/2026, 5:16:49 AM

GitHub: 901 stars · 131 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 megvii-research/mdistiller, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, specifying the chosen open-source license (e.g., MIT, Apache-2.0, or a custom license if applicable) to clarify usage rights.
  • highhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to `https://arxiv.org/abs/2203.08679` (or the most appropriate official project page) in the GitHub repository settings.
  • mediumabout#3
    Refine the repository description to emphasize its library nature

    Why:

    CURRENT
    The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf
    COPY-PASTE FIX
    MDistiller is a PyTorch library providing classical and state-of-the-art knowledge distillation algorithms for computer vision benchmarks. It includes official implementations of [CVPR2022] Decoupled Knowledge Distillation and [ICCV2023] DOT: A Distillation-Oriented Trainer.

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 megvii-research/mdistiller
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 4×
  2. pytorch/vision · recommended 3×
  3. IntelAI/distiller · recommended 2×
  4. huggingface/transformers · recommended 2×
  5. yoshitomo-matsubara/torchdistill · recommended 1×
  • CATEGORY QUERY
    Seeking a PyTorch library for implementing advanced knowledge distillation in computer vision models.
    you: not recommended
    AI recommended (in order):
    1. Distiller (IntelAI/distiller)
    2. TorchDistill (yoshitomo-matsubara/torchdistill)
    3. MMDistillation (open-mmlab/mmdistillation)
    4. PyTorch-Knowledge-Distillation (wuyangli/pytorch-knowledge-distillation)
    5. PaddleSlim (PaddlePaddle/PaddleSlim)

    AI recommended 5 alternatives but never named megvii-research/mdistiller. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I improve the performance of smaller neural networks using knowledge transfer techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. torch.nn (pytorch/pytorch)
    3. tf.keras.Model (tensorflow/tensorflow)
    4. torchvision.models (pytorch/vision)
    5. tf.keras.applications (tensorflow/tensorflow)
    6. Distiller (Intel AI) (IntelAI/distiller)
    7. Hugging Face Transformers (huggingface/transformers)
    8. torchvision.models (pytorch/vision)
    9. tf.keras.applications (tensorflow/tensorflow)
    10. AutoKeras (keras-team/autokeras)
    11. NNI (Neural Network Intelligence) by Microsoft (microsoft/nni)
    12. torchvision.models (pytorch/vision)
    13. tf.keras.applications (tensorflow/tensorflow)

    AI recommended 13 alternatives but never named megvii-research/mdistiller. 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 megvii-research/mdistiller?
    pass
    AI named megvii-research/mdistiller explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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