RRepoGEO

REPOGEO REPORT · LITE

jolibrain/deepdetect

Default branch master · commit ab3b96ae · scanned 5/25/2026, 11:21:43 AM

GitHub: 2,549 stars · 550 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 jolibrain/deepdetect, 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
    Reposition the README's opening paragraph to emphasize its role as an inference server and deployment platform

    Why:

    CURRENT
    DeepDetect (https://www.deepdetect.com/) is a machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications. It has support for both training and inference, with automatic conversion to embedded platforms with TensorRT (NVidia GPU) and NCNN (ARM CPU).
    COPY-PASTE FIX
    DeepDetect (https://www.deepdetect.com/) is a high-performance, open-source **Deep Learning Inference Server and Machine Learning Deployment Platform** written in C++11. It provides a unified API to easily serve and integrate state-of-the-art machine learning models into production applications, supporting both training and inference across diverse frameworks like PyTorch, TensorFlow, and XGBoost, with automatic conversion for embedded platforms (TensorRT, NCNN).
  • mediumtopics#2
    Add more specific topics related to model serving and deployment

    Why:

    CURRENT
    caffe, deep-learning, gpu, image-classification, image-search, image-segmentation, machine-learning, ncnn, neural-nets, object-detection, pytorch, rest-api, tensorrt, tensorrt-conversion, tensorrt-inference, time-series, tsne, xgboost
    COPY-PASTE FIX
    caffe, deep-learning, gpu, image-classification, image-search, image-segmentation, machine-learning, ncnn, neural-nets, object-detection, pytorch, rest-api, tensorrt, tensorrt-conversion, tensorrt-inference, time-series, tsne, xgboost, inference-server, model-serving, ml-deployment, production-ml
  • lowlicense#3
    Clarify the specific license(s) in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, for example: 'DeepDetect is licensed under [License Name(s)]. Please refer to the LICENSE file for full details.'

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 jolibrain/deepdetect
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Triton Inference Server
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Triton Inference Server · recommended 1×
  2. TorchServe · recommended 1×
  3. TensorFlow Serving · recommended 1×
  4. ONNX Runtime Server · recommended 1×
  5. KServe · recommended 1×
  • CATEGORY QUERY
    How can I easily integrate a deep learning inference server into my application?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server
    2. TorchServe
    3. TensorFlow Serving
    4. ONNX Runtime Server
    5. KServe
    6. MLflow

    AI recommended 6 alternatives but never named jolibrain/deepdetect. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What platform simplifies deploying diverse machine learning models to embedded and GPU environments?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA JetPack SDK
    2. OpenVINO Toolkit
    3. TensorFlow Lite
    4. ONNX Runtime
    5. PyTorch Mobile
    6. Edge Impulse

    AI recommended 6 alternatives but never named jolibrain/deepdetect. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 jolibrain/deepdetect?
    pass
    AI named jolibrain/deepdetect explicitly

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

  • If a team adopts jolibrain/deepdetect in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jolibrain/deepdetect 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 jolibrain/deepdetect solve, and who is the primary audience?
    pass
    AI named jolibrain/deepdetect 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
  • Prioritized action items8 vs 3 in Lite