Repository Analysis

lucidrains/vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

4.6 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of lucidrains/vit-pytorch, a Python project with 25,415 GitHub stars. SynthScan v2.0 examined 21,407 lines of code across 83 source files, recording 99 pattern matches distributed across 5 syntactic categories. The overall adjusted score of 4.6 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

4.6
Adjusted Score
4.6
Raw Score
100%
Time Factor
2026-06-22
Last Push
25.4K
Stars
Python
Language
21.4K
Lines of Code
83
Files
99
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 0LOW 99

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 99 distinct pattern matches across 5 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Unused Imports54 hits · 54 pts
SeverityFileLineSnippetContext
LOWtrain_vit_decorr.py9CODE
LOWvit_pytorch/max_vit.py1CODE
LOWvit_pytorch/max_vit.py6CODE
LOWvit_pytorch/cross_vit.py3CODE
LOWvit_pytorch/vit_nd.py1CODE
LOWvit_pytorch/vit_with_keel_post_ln.py1CODE
LOWvit_pytorch/look_vit.py6CODE
LOWvit_pytorch/nest.py1CODE
LOWvit_pytorch/vit_nd_pope.py1CODE
LOWvit_pytorch/na_vit_nested_tensor_3d.py1CODE
LOWvit_pytorch/crossformer.py5CODE
LOWvit_pytorch/deepvit.py3CODE
LOWvit_pytorch/efficient.py3CODE
LOWvit_pytorch/vat.py1CODE
LOWvit_pytorch/cvt.py3CODE
LOWvit_pytorch/cvt.py5CODE
LOWvit_pytorch/twins_svt.py3CODE
LOWvit_pytorch/twins_svt.py5CODE
LOWvit_pytorch/vit_5.py5CODE
LOWvit_pytorch/normalized_vit.py7CODE
LOWvit_pytorch/recorder.py1CODE
LOWvit_pytorch/levit.py5CODE
LOWvit_pytorch/levit.py7CODE
LOWvit_pytorch/vit_for_small_dataset.py1CODE
LOWvit_pytorch/accept_video_wrapper.py1CODE
LOWvit_pytorch/accept_video_wrapper.py12CODE
LOWvit_pytorch/__init__.py1CODE
LOWvit_pytorch/__init__.py2CODE
LOWvit_pytorch/__init__.py4CODE
LOWvit_pytorch/__init__.py5CODE
LOWvit_pytorch/na_vit_nested_tensor.py1CODE
LOWvit_pytorch/wwt.py1CODE
LOWvit_pytorch/wwt.py7CODE
LOWvit_pytorch/extractor.py1CODE
LOWvit_pytorch/scalable_vit.py5CODE
LOWvit_pytorch/scalable_vit.py6CODE
LOWvit_pytorch/local_vit.py4CODE
LOWvit_pytorch/vivit.py1CODE
LOWvit_pytorch/distill.py10CODE
LOWvit_pytorch/jet_vit.py1CODE
LOWvit_pytorch/vat_siglip.py1CODE
LOWvit_pytorch/vit_with_patch_merger.py4CODE
LOWvit_pytorch/vaat.py3CODE
LOWvit_pytorch/na_vit.py1CODE
LOWvit_pytorch/vit_detpool.py1CODE
LOWvit_pytorch/dino.py7CODE
LOWvit_pytorch/pit.py5CODE
LOWvit_pytorch/rvt.py1CODE
LOWvit_pytorch/cait.py4CODE
LOWvit_pytorch/vit_nd_rotary.py1CODE
LOWvit_pytorch/max_vit_with_registers.py1CODE
LOWvit_pytorch/simple_vit_with_specialized_cls.py1CODE
LOWvit_pytorch/vit_with_decorr.py6CODE
LOWvit_pytorch/simple_vit_attn_residual.py3CODE
AI Structural Patterns35 hits · 35 pts
SeverityFileLineSnippetContext
LOWvit_pytorch/cross_vit.py205CODE
LOWvit_pytorch/look_vit.py141CODE
LOWvit_pytorch/vit_nd_pope.py201CODE
LOWvit_pytorch/lejepa.py189CODE
LOWvit_pytorch/crossformer.py209CODE
LOWvit_pytorch/deepvit.py70CODE
LOWvit_pytorch/simple_flash_attn_vit_3d.py79CODE
LOWvit_pytorch/vat.py261CODE
LOWvit_pytorch/cvt.py115CODE
LOWvit_pytorch/twins_svt.py179CODE
LOWvit_pytorch/cct.py160CODE
LOWvit_pytorch/cct.py163CODE
LOWvit_pytorch/cct.py210CODE
LOWvit_pytorch/cct.py307CODE
LOWvit_pytorch/accept_video_wrapper.py28CODE
LOWvit_pytorch/simple_flash_attn_vit.py84CODE
LOWvit_pytorch/wwt.py310CODE
LOWvit_pytorch/regionvit.py195CODE
LOWvit_pytorch/scalable_vit.py197CODE
LOWvit_pytorch/scalable_vit.py241CODE
LOWvit_pytorch/cct_3d.py160CODE
LOWvit_pytorch/cct_3d.py163CODE
LOWvit_pytorch/cct_3d.py229CODE
LOWvit_pytorch/cct_3d.py326CODE
LOWvit_pytorch/mpp.py80CODE
LOWvit_pytorch/vivit_with_moss.py279CODE
LOWvit_pytorch/vat_siglip.py171CODE
LOWvit_pytorch/t2t.py27CODE
LOWvit_pytorch/vaat.py209CODE
LOWvit_pytorch/vaat.py422CODE
LOWvit_pytorch/dino.py185CODE
LOWvit_pytorch/max_vit_with_registers.py201CODE
LOWvit_pytorch/es_vit.py224CODE
LOWvit_pytorch/vit_with_decorr.py29CODE
LOWvit_pytorch/vit_with_decorr.py191CODE
Hyper-Verbose Identifiers5 hits · 5 pts
SeverityFileLineSnippetContext
LOWvit_pytorch/cvt.py18def group_by_key_prefix_and_remove_prefix(prefix, d):CODE
LOWvit_pytorch/twins_svt.py18def group_by_key_prefix_and_remove_prefix(prefix, d):CODE
LOWvit_pytorch/learnable_memory_vit.py18def set_module_requires_grad_(module, requires_grad):CODE
LOWvit_pytorch/mpp.py18def get_mask_subset_with_prob(patched_input, prob):CODE
LOWvit_pytorch/na_vit.py38def group_images_by_max_seq_len(CODE
AI Slop Vocabulary3 hits · 4 pts
SeverityFileLineSnippetContext
LOWvit_pytorch/vat.py278 add_self_attn = True, # in the paper, they didn't have any ways for the action token to exchange information wiCODE
LOWvit_pytorch/vaat.py441 add_self_attn = True, # in the paper, they didn't have any ways for the action token to exchange information wiCODE
LOWvit_pytorch/vit_with_decorr.py2# but instead of their decorr module updated with SGD, remove all projections and just return a decorrelation auxiliary COMMENT
Modern Structural Boilerplate2 hits · 2 pts
SeverityFileLineSnippetContext
LOWvit_pytorch/cct.py20__all__ = ['cct_2', 'cct_4', 'cct_6', 'cct_7', 'cct_8', 'cct_14', 'cct_16']CODE
LOWvit_pytorch/cct_3d.py20__all__ = ['cct_2', 'cct_4', 'cct_6', 'cct_7', 'cct_8', 'cct_14', 'cct_16']CODE