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REPOGEO REPORT · LITE

davidtvs/pytorch-lr-finder

Default branch master · commit 76df3050 · scanned 5/17/2026, 12:37:21 PM

GitHub: 1,006 stars · 121 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 davidtvs/pytorch-lr-finder, 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
  • highhomepage#1
    Add the PyPI project page as the repository homepage

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/torch-lr-finder/
  • highreadme#2
    Refine the README's introductory paragraph to emphasize standalone utility

    Why:

    CURRENT
    A PyTorch implementation of the learning rate range test detailed in Cyclical Learning Rates for Training Neural Networks by Leslie N. Smith and the tweaked version used by fastai.
    COPY-PASTE FIX
    This is a **standalone PyTorch library** that implements the learning rate range test, a crucial technique detailed in Cyclical Learning Rates for Training Neural Networks by Leslie N. Smith and further refined by fastai. It provides a focused, minimal-overhead utility to efficiently determine optimal learning rates for your models, integrating seamlessly into *any* existing PyTorch training loop without requiring adoption of a larger framework.
  • mediumtopics#3
    Expand topics to include more specific terms

    Why:

    CURRENT
    learning-rate, pytorch
    COPY-PASTE FIX
    learning-rate, pytorch, deep-learning, neural-networks, machine-learning, lr-finder, utility, tool, training

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 davidtvs/pytorch-lr-finder
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 4×
  2. tensorflow/tensorflow · recommended 4×
  3. torch_lr_finder · recommended 2×
  4. scikit-learn/scikit-learn · recommended 2×
  5. torch.optim.lr_scheduler.OneCycleLR · recommended 1×
  • CATEGORY QUERY
    How can I find an optimal learning rate for my deep learning model in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. torch_lr_finder
    2. torch.optim.lr_scheduler.OneCycleLR
    3. torch.optim.lr_scheduler.StepLR
    4. torch.optim.lr_scheduler.ExponentialLR
    5. torch.optim.lr_scheduler.ReduceLROnPlateau
    6. torch.optim.lr_scheduler.CosineAnnealingLR
    7. Optuna
    8. Ray Tune
    9. Weights & Biases (W&B) Sweeps

    AI recommended 9 alternatives but never named davidtvs/pytorch-lr-finder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good methods for performing a learning rate range test in neural networks?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. torch_lr_finder
    3. Keras (keras-team/keras)
    4. TensorFlow (tensorflow/tensorflow)
    5. tf.keras.callbacks.LearningRateScheduler (tensorflow/tensorflow)
    6. keras-lr-finder
    7. Scikit-learn (scikit-learn/scikit-learn)
    8. sklearn.model_selection.RandomizedSearchCV (scikit-learn/scikit-learn)
    9. Hyperopt (hyperopt/hyperopt)
    10. Optuna (optuna/optuna)
    11. Ray Tune (ray-project/ray)
    12. ReduceLROnPlateau
    13. CosineAnnealingLR
    14. torch.optim.lr_scheduler.ReduceLROnPlateau (pytorch/pytorch)
    15. torch.optim.lr_scheduler.CosineAnnealingLR (pytorch/pytorch)
    16. torch.optim.lr_scheduler.OneCycleLR (pytorch/pytorch)
    17. tf.keras.callbacks.ReduceLROnPlateau (tensorflow/tensorflow)
    18. tf.keras.optimizers.schedules.CosineDecay (tensorflow/tensorflow)

    AI recommended 18 alternatives but never named davidtvs/pytorch-lr-finder. 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 davidtvs/pytorch-lr-finder?
    pass
    AI named davidtvs/pytorch-lr-finder explicitly

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

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

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

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davidtvs/pytorch-lr-finder — 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