Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
This report summarizes scanner evidence for lucidrains/vit-pytorch, a Python project with 25,543 GitHub stars. SynthScan examined 22,977 lines of code across 88 source files, recording 112 pattern matches distributed across 7 syntactic categories. The legacy adjusted pattern signal is 5.2 (Moderate pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 50 finding(s).
Are imports declared and package/API names verified? 55 finding(s).
Are wrappers, guards, and repeated structures necessary? 54 finding(s).
Which changes need focused human review? 112 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 112 distinct pattern matches across 7 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | train_vit_decorr.py | 9 | CODE | |
| LOW | vit_pytorch/levjepa_nd.py | 4 | CODE | |
| LOW | vit_pytorch/max_vit.py | 1 | CODE | |
| LOW | vit_pytorch/max_vit.py | 6 | CODE | |
| LOW | vit_pytorch/levjepa.py | 3 | CODE | |
| LOW | vit_pytorch/cross_vit.py | 3 | CODE | |
| LOW | vit_pytorch/vit_nd.py | 1 | CODE | |
| LOW | vit_pytorch/vit_with_keel_post_ln.py | 1 | CODE | |
| LOW | vit_pytorch/look_vit.py | 6 | CODE | |
| LOW | vit_pytorch/nest.py | 1 | CODE | |
| LOW | vit_pytorch/vit_nd_pope.py | 1 | CODE | |
| LOW | vit_pytorch/na_vit_nested_tensor_3d.py | 1 | CODE | |
| LOW | vit_pytorch/crossformer.py | 5 | CODE | |
| LOW | vit_pytorch/deepvit.py | 3 | CODE | |
| LOW | vit_pytorch/efficient.py | 3 | CODE | |
| LOW | vit_pytorch/vat.py | 1 | CODE | |
| LOW | vit_pytorch/cvt.py | 3 | CODE | |
| LOW | vit_pytorch/cvt.py | 5 | CODE | |
| LOW | vit_pytorch/twins_svt.py | 3 | CODE | |
| LOW | vit_pytorch/twins_svt.py | 5 | CODE | |
| LOW | vit_pytorch/vit_5.py | 5 | CODE | |
| LOW | vit_pytorch/normalized_vit.py | 7 | CODE | |
| LOW | vit_pytorch/recorder.py | 1 | CODE | |
| LOW | vit_pytorch/levit.py | 5 | CODE | |
| LOW | vit_pytorch/levit.py | 7 | CODE | |
| LOW | vit_pytorch/vit_for_small_dataset.py | 1 | CODE | |
| LOW | vit_pytorch/accept_video_wrapper.py | 1 | CODE | |
| LOW | vit_pytorch/accept_video_wrapper.py | 12 | CODE | |
| LOW | vit_pytorch/__init__.py | 1 | CODE | |
| LOW | vit_pytorch/__init__.py | 2 | CODE | |
| LOW | vit_pytorch/__init__.py | 4 | CODE | |
| LOW | vit_pytorch/__init__.py | 5 | CODE | |
| LOW | vit_pytorch/na_vit_nested_tensor.py | 1 | CODE | |
| LOW | vit_pytorch/wwt.py | 1 | CODE | |
| LOW | vit_pytorch/wwt.py | 7 | CODE | |
| LOW | vit_pytorch/extractor.py | 1 | CODE | |
| LOW | vit_pytorch/scalable_vit.py | 5 | CODE | |
| LOW | vit_pytorch/scalable_vit.py | 6 | CODE | |
| LOW | vit_pytorch/local_vit.py | 4 | CODE | |
| LOW | vit_pytorch/vivit.py | 1 | CODE | |
| LOW | vit_pytorch/distill.py | 10 | CODE | |
| LOW | vit_pytorch/jet_vit.py | 1 | CODE | |
| LOW | vit_pytorch/vat_siglip.py | 1 | CODE | |
| LOW | vit_pytorch/vit_with_patch_merger.py | 4 | CODE | |
| LOW | vit_pytorch/vaat.py | 3 | CODE | |
| LOW | vit_pytorch/na_vit.py | 1 | CODE | |
| LOW | vit_pytorch/vit_detpool.py | 1 | CODE | |
| LOW | vit_pytorch/pit.py | 5 | CODE | |
| LOW | vit_pytorch/rvt.py | 1 | CODE | |
| LOW | vit_pytorch/cait.py | 4 | CODE | |
| LOW | vit_pytorch/vit_nd_rotary.py | 1 | CODE | |
| LOW | vit_pytorch/max_vit_with_registers.py | 1 | CODE | |
| LOW | vit_pytorch/simple_vit_with_specialized_cls.py | 1 | CODE | |
| LOW | vit_pytorch/vit_with_decorr.py | 6 | CODE | |
| LOW | vit_pytorch/simple_vit_attn_residual.py | 3 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | vit_pytorch/levjepa_nd.py | 351 | CODE | |
| LOW | vit_pytorch/levjepa.py | 359 | CODE | |
| LOW | vit_pytorch/cross_vit.py | 205 | CODE | |
| LOW | vit_pytorch/look_vit.py | 141 | CODE | |
| LOW | vit_pytorch/vit_nd_pope.py | 195 | CODE | |
| LOW | vit_pytorch/lejepa.py | 211 | CODE | |
| LOW | vit_pytorch/crossformer.py | 209 | CODE | |
| LOW | vit_pytorch/deepvit.py | 70 | CODE | |
| LOW | vit_pytorch/simple_flash_attn_vit_3d.py | 79 | CODE | |
| LOW | vit_pytorch/vat.py | 261 | CODE | |
| LOW | vit_pytorch/cvt.py | 115 | CODE | |
| LOW | vit_pytorch/twins_svt.py | 179 | CODE | |
| LOW | vit_pytorch/cct.py | 160 | CODE | |
| LOW | vit_pytorch/cct.py | 163 | CODE | |
| LOW | vit_pytorch/cct.py | 210 | CODE | |
| LOW | vit_pytorch/cct.py | 307 | CODE | |
| LOW | vit_pytorch/accept_video_wrapper.py | 28 | CODE | |
| LOW | vit_pytorch/simple_flash_attn_vit.py | 84 | CODE | |
| LOW | vit_pytorch/wwt.py | 310 | CODE | |
| LOW | vit_pytorch/regionvit.py | 195 | CODE | |
| LOW | vit_pytorch/scalable_vit.py | 197 | CODE | |
| LOW | vit_pytorch/scalable_vit.py | 241 | CODE | |
| LOW | vit_pytorch/cct_3d.py | 160 | CODE | |
| LOW | vit_pytorch/cct_3d.py | 163 | CODE | |
| LOW | vit_pytorch/cct_3d.py | 229 | CODE | |
| LOW | vit_pytorch/cct_3d.py | 326 | CODE | |
| LOW | vit_pytorch/mpp.py | 80 | CODE | |
| LOW | vit_pytorch/vivit_with_moss.py | 279 | CODE | |
| LOW | vit_pytorch/vat_siglip.py | 171 | CODE | |
| LOW | vit_pytorch/t2t.py | 27 | CODE | |
| LOW | vit_pytorch/vaat.py | 209 | CODE | |
| LOW | vit_pytorch/vaat.py | 422 | CODE | |
| LOW | vit_pytorch/dino.py | 185 | CODE | |
| LOW | vit_pytorch/vit_nd_rotary.py | 171 | CODE | |
| LOW | vit_pytorch/max_vit_with_registers.py | 201 | CODE | |
| LOW | vit_pytorch/es_vit.py | 224 | CODE | |
| LOW | vit_pytorch/vit_with_decorr.py | 29 | CODE | |
| LOW | vit_pytorch/vit_with_decorr.py | 186 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | vit_pytorch/levjepa_nd.py | 92 | except Exception: | CODE |
| HIGH | vit_pytorch/levjepa.py | 50 | except Exception: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_dino_loss.py | 42 | def test_loss_and_gradient_stay_finite_for_low_precision_logits(dtype): | CODE |
| LOW | tests/test_dino_loss.py | 63 | def test_loss_gradient_matches_log_softmax_reference(): | CODE |
| LOW | vit_pytorch/levjepa_nd.py | 57 | def create_nd_block_causal_mask_fn(coords, causal_dims): | CODE |
| LOW | vit_pytorch/levjepa.py | 40 | def create_block_causal_mask_fn(frame_idx): | CODE |
| LOW | vit_pytorch/cvt.py | 18 | def group_by_key_prefix_and_remove_prefix(prefix, d): | CODE |
| LOW | vit_pytorch/twins_svt.py | 18 | def group_by_key_prefix_and_remove_prefix(prefix, d): | CODE |
| LOW | vit_pytorch/learnable_memory_vit.py | 18 | def set_module_requires_grad_(module, requires_grad): | CODE |
| LOW | vit_pytorch/mpp.py | 18 | def get_mask_subset_with_prob(patched_input, prob): | CODE |
| LOW | vit_pytorch/na_vit.py | 38 | def group_images_by_max_seq_len( | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | vit_pytorch/vat.py | 278 | add_self_attn = True, # in the paper, they didn't have any ways for the action token to exchange information wi | CODE |
| LOW | vit_pytorch/vaat.py | 441 | add_self_attn = True, # in the paper, they didn't have any ways for the action token to exchange information wi | CODE |
| LOW | vit_pytorch/vit_with_decorr.py | 2 | # but instead of their decorr module updated with SGD, remove all projections and just return a decorrelation auxiliary | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | vit_pytorch/levjepa_nd.py | 92 | except Exception: | CODE |
| LOW | vit_pytorch/levjepa_nd.py | 317 | except Exception: | CODE |
| LOW | vit_pytorch/levjepa.py | 50 | except Exception: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | vit_pytorch/cct.py | 20 | __all__ = ['cct_2', 'cct_4', 'cct_6', 'cct_7', 'cct_8', 'cct_14', 'cct_16'] | CODE |
| LOW | vit_pytorch/cct_3d.py | 20 | __all__ = ['cct_2', 'cct_4', 'cct_6', 'cct_7', 'cct_8', 'cct_14', 'cct_16'] | CODE |