Repository Analysis

facebookresearch/vggt

[CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer

29.8 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of facebookresearch/vggt, a Python project with 14,121 GitHub stars. SynthScan v2.0 examined 16,113 lines of code across 81 source files, recording 288 pattern matches distributed across 17 syntactic categories. The overall adjusted score of 29.8 places this repository in the Moderate AI signal 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).

29.8
Adjusted Score
29.8
Raw Score
100%
Time Factor
2026-05-19
Last Push
14.1K
Stars
Python
Language
16.1K
Lines of Code
81
Files
288
Pattern Hits
2026-08-02
Scan Date
0.19
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 15MEDIUM 55LOW 218

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 288 distinct pattern matches across 17 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.

Decorative Section Separators45 hits · 142 pts
SeverityFileLineSnippetContext
MEDIUMdemo_gradio.py100# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py102# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py41# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py43# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py168# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py170# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py183# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py185# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py256# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py258# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py329# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py331# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py343# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py345# -------------------------------------------------------------------------COMMENT
MEDIUMdemo_gradio.py542 # -------------------------------------------------------------------------STRING
MEDIUMdemo_gradio.py548 # -------------------------------------------------------------------------STRING
MEDIUMdemo_gradio.py568 # -------------------------------------------------------------------------STRING
MEDIUMdemo_gradio.py570 # -------------------------------------------------------------------------STRING
MEDIUMdemo_gradio.py677 # -------------------------------------------------------------------------STRING
MEDIUMdemo_gradio.py679 # -------------------------------------------------------------------------STRING
MEDIUMtraining/train_utils/checkpoint.py25# ------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/checkpoint.py27# ------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/freeze.py13# ------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/freeze.py15# ------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/freeze.py58# ------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/freeze.py60# ------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py29 # ---------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py31 # ---------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py59# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py61# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py222 # ──────────────────────────────────────────────────────────────────COMMENT
MEDIUMtraining/train_utils/optimizer.py224 # ──────────────────────────────────────────────────────────────────COMMENT
MEDIUMtraining/train_utils/optimizer.py229 # ──────────────────────────────────────────────────────────────────COMMENT
MEDIUMtraining/train_utils/optimizer.py231 # ──────────────────────────────────────────────────────────────────COMMENT
MEDIUMtraining/train_utils/optimizer.py16# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py18# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py82# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py84# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py156# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py158# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py203# -----------------------------------------------------------------------------COMMENT
MEDIUMtraining/train_utils/optimizer.py205# -----------------------------------------------------------------------------COMMENT
MEDIUMvggt/dependency/projection.py29 # 1. perspective divide ───────────────────────────────────────COMMENT
MEDIUMvggt/dependency/projection.py34 # 2. optional distortion ──────────────────────────────────────COMMENT
MEDIUMvggt/dependency/projection.py39 # 3. homogeneous coords then K multiplication ─────────────────COMMENT
Unused Imports101 hits · 100 pts
SeverityFileLineSnippetContext
LOWdemo_viser.py12CODE
LOWdemo_colmap.py21CODE
LOWvisual_util.py8CODE
LOWtraining/launch.py9CODE
LOWtraining/launch.py9CODE
LOWtraining/trainer.py40CODE
LOWtraining/train_utils/logging.py9CODE
LOWtraining/train_utils/distributed.py9CODE
LOWtraining/train_utils/distributed.py10CODE
LOWtraining/train_utils/optimizer.py9CODE
LOWtraining/train_utils/gradient_clip.py9CODE
LOWtraining/train_utils/general.py13CODE
LOWtraining/train_utils/general.py18CODE
LOWtraining/train_utils/general.py18CODE
LOWtraining/data/base_dataset.py11CODE
LOWtraining/data/dynamic_dataloader.py12CODE
LOWtraining/data/dynamic_dataloader.py12CODE
LOWtraining/data/dynamic_dataloader.py13CODE
LOWtraining/data/track_util.py15CODE
LOWtraining/data/composed_dataset.py16CODE
LOWtraining/data/composed_dataset.py17CODE
LOWtraining/data/datasets/co3d.py10CODE
LOWtraining/data/datasets/co3d.py18CODE
LOWtraining/data/datasets/vkitti.py7CODE
LOWtraining/data/datasets/vkitti.py16CODE
LOWvggt/layers/attention.py10CODE
LOWvggt/layers/attention.py11CODE
LOWvggt/layers/attention.py12CODE
LOWvggt/layers/swiglu_ffn.py8CODE
LOWvggt/layers/__init__.py7CODE
LOWvggt/layers/__init__.py8CODE
LOWvggt/layers/__init__.py9CODE
LOWvggt/layers/__init__.py9CODE
LOWvggt/layers/__init__.py10CODE
LOWvggt/layers/__init__.py11CODE
LOWvggt/layers/rope.py17CODE
LOWvggt/layers/block.py10CODE
LOWvggt/layers/block.py11CODE
LOWvggt/layers/block.py13CODE
LOWvggt/dependency/track_predict.py9CODE
LOWvggt/dependency/vggsfm_utils.py9CODE
LOWvggt/dependency/vggsfm_utils.py9CODE
LOWvggt/dependency/vggsfm_utils.py9CODE
LOWvggt/dependency/vggsfm_utils.py9CODE
LOWvggt/dependency/vggsfm_utils.py9CODE
LOWvggt/dependency/vggsfm_utils.py11CODE
LOWvggt/dependency/vggsfm_utils.py12CODE
LOWvggt/dependency/__init__.py1CODE
LOWvggt/dependency/__init__.py2CODE
LOWvggt/dependency/__init__.py2CODE
LOWvggt/dependency/__init__.py3CODE
LOWvggt/dependency/vggsfm_tracker.py8CODE
LOWvggt/dependency/vggsfm_tracker.py12CODE
LOWvggt/dependency/vggsfm_tracker.py13CODE
LOWvggt/dependency/vggsfm_tracker.py14CODE
LOWvggt/dependency/vggsfm_tracker.py14CODE
LOWvggt/dependency/vggsfm_tracker.py15CODE
LOWvggt/dependency/vggsfm_tracker.py15CODE
LOWvggt/dependency/vggsfm_tracker.py17CODE
LOWvggt/dependency/vggsfm_tracker.py18CODE
41 more matches not shown…
Docstring Block Structure11 hits · 55 pts
SeverityFileLineSnippetContext
HIGHvisual_util.py29 Converts VGGT predictions to a 3D scene represented as a GLB file. Args: predictions (dict): DictionarSTRING
HIGHtraining/data/base_dataset.py76 Abstract method to retrieve data for a given sequence. Args: seq_index (int, optional): InSTRING
HIGHtraining/data/base_dataset.py238 TODO: add the function to sample the ids by pose similarity ranking. Sample a set of IDs from a sequenSTRING
HIGHtraining/data/augmentation.py16Create a composition of image augmentations. Args: color_jitter: Dictionary containing color jitter parametSTRING
HIGHtraining/data/composed_dataset.py215 Retrieves an item using either an integer index or a tuple index. Args: idx (int or tuple)STRING
HIGHtraining/data/dataset_util.py29 TODO: some names of width and height seem not consistent. Need to check. Crops the given image and deSTRING
HIGHtraining/data/dataset_util.py172 Resizes the given image and depth map (if provided) to slightly larger than `target_shape`, updating the intrinSTRING
HIGHtraining/data/dataset_util.py654 Reads a depth map from disk in either .exr or .png format. The .exr is loaded using OpenCV with the environmentSTRING
HIGHvggt/layers/rope.py155Applies 2D rotary position embeddings to input tokens. Args: tokens: Input tensor of shape (batch_sSTRING
HIGHvggt/utils/load_fn.py14 Load and preprocess images by center padding to square and resizing to target size. Also returns the position iSTRING
HIGHvggt/utils/load_fn.py98 A quick start function to load and preprocess images for model input. This assumes the images should have the sSTRING
Hyper-Verbose Identifiers53 hits · 52 pts
SeverityFileLineSnippetContext
LOWdemo_colmap.py254def rename_colmap_recons_and_rescale_camera(CODE
LOWvisual_util.py218def integrate_camera_into_scene(scene: trimesh.Scene, transform: np.ndarray, face_colors: tuple, scene_scale: float):CODE
LOWvisual_util.py287def get_opengl_conversion_matrix() -> np.ndarray:CODE
LOWtraining/loss.py370def gradient_loss_multi_scale_wrapper(prediction, target, mask, scales=4, gradient_loss_fn = None, conf=None):CODE
LOWtraining/trainer.py182 def _setup_torch_dist_and_backend(self, cuda_conf: Dict, distributed_conf: Dict) -> None:CODE
LOWtraining/trainer.py198 def _load_resuming_checkpoint(self, ckpt_path: str):CODE
LOWtraining/trainer.py289 def _setup_ddp_distributed_training(self, distributed_conf: Dict, device: str):CODE
LOWtraining/trainer.py638 def _run_steps_on_batch_chunks(CODE
LOWtraining/trainer.py823def chunk_batch_for_accum_steps(batch: Mapping, accum_steps: int) -> List[Mapping]:CODE
LOWtraining/trainer.py829def is_sequence_of_primitives(data: Any) -> bool:CODE
LOWtraining/train_utils/distributed.py12def get_machine_local_and_dist_rank():CODE
LOWtraining/train_utils/freeze.py91def _check_every_pattern_used(matched_names: set[str], patterns: List[str]):CODE
LOWtraining/train_utils/optimizer.py41 def _validate_optimizer_schedulers(self):CODE
LOWtraining/train_utils/optimizer.py64def validate_param_group_params(param_groups: List[Dict], model: nn.Module):CODE
LOWtraining/train_utils/optimizer.py100def get_module_cls_to_param_names(model: nn.Module) -> Dict[type, Set[str]]:CODE
LOWtraining/train_utils/optimizer.py111def unix_param_pattern_to_parameter_names(filter_param_names: Union[List[str], None],CODE
LOWtraining/train_utils/optimizer.py125def unix_module_cls_pattern_to_parameter_names(filter_module_cls_names: Union[List[str], None],CODE
LOWtraining/train_utils/optimizer.py142def _unix_pattern_to_parameter_names(scheduler_cfg,CODE
LOWtraining/train_utils/optimizer.py180def name_constraints_to_parameters(param_constraints: List[Set[str]],CODE
LOWtraining/train_utils/optimizer.py186def map_scheduler_cfgs_to_param_groups(all_scheduler_cfgs: Iterable[List[dict]],CODE
LOWtraining/train_utils/normalization.py27def normalize_camera_extrinsics_and_points_batch(CODE
LOWtraining/train_utils/general.py243def is_dist_avail_and_initialized():CODE
LOWtraining/data/worker_fn.py22def is_dist_avail_and_initialized():CODE
LOWtraining/data/track_util.py281def get_track_colors_by_position(CODE
LOWtraining/data/track_util.py335def visualize_tracks_on_images(CODE
LOWtraining/data/dataset_util.py26def crop_image_depth_and_intrinsic_by_pp(CODE
LOWtraining/data/dataset_util.py161def resize_image_depth_and_intrinsic(CODE
LOWtraining/data/dataset_util.py317def depth_to_world_coords_points(CODE
LOWtraining/data/dataset_util.py369def depth_to_cam_coords_points(CODE
LOWvggt/layers/vision_transformer.py214 def prepare_tokens_with_masks(self, x, masks=None):CODE
LOWvggt/layers/vision_transformer.py273 def _get_intermediate_layers_not_chunked(self, x, n=1):CODE
LOWvggt/layers/vision_transformer.py285 def _get_intermediate_layers_chunked(self, x, n=1):CODE
LOWvggt/layers/rope.py86 def _compute_frequency_components(CODE
LOWvggt/layers/block.py101def drop_add_residual_stochastic_depth(CODE
LOWvggt/layers/block.py175def drop_add_residual_stochastic_depth_list(CODE
LOWvggt/dependency/track_predict.py232def _augment_non_visible_frames(CODE
LOWvggt/dependency/vggsfm_utils.py189def initialize_feature_extractors(max_query_num, det_thres=0.005, extractor_method="aliked", device="cuda"):CODE
LOWvggt/dependency/np_to_pycolmap.py12def batch_np_matrix_to_pycolmap(CODE
LOWvggt/dependency/np_to_pycolmap.py148def pycolmap_to_batch_np_matrix(reconstruction, device="cpu", camera_type="SIMPLE_PINHOLE"):CODE
LOWvggt/dependency/np_to_pycolmap.py201def batch_np_matrix_to_pycolmap_wo_track(CODE
LOWvggt/utils/visual_track.py37def get_track_colors_by_position(tracks_b, vis_mask_b=None, image_width=None, image_height=None, cmap_name="hsv"):CODE
LOWvggt/utils/visual_track.py80def visualize_tracks_on_images(CODE
LOWvggt/utils/load_fn.py13def load_and_preprocess_images_square(image_path_list, target_size=1024):CODE
LOWvggt/utils/load_fn.py97def load_and_preprocess_images(image_path_list, mode="crop"):CODE
LOWvggt/utils/pose_enc.py11def extri_intri_to_pose_encoding(CODE
LOWvggt/utils/pose_enc.py62def pose_encoding_to_extri_intri(CODE
LOWvggt/utils/geometry.py15def unproject_depth_map_to_point_map(CODE
LOWvggt/utils/geometry.py47def depth_to_world_coords_points(CODE
LOWvggt/utils/geometry.py87def depth_to_cam_coords_points(depth_map: np.ndarray, intrinsic: np.ndarray) -> tuple[np.ndarray, np.ndarray]:CODE
LOWvggt/utils/geometry.py175def project_world_points_to_camera_points_batch(world_points, cam_extrinsics):CODE
LOWvggt/utils/geometry.py204def project_world_points_to_cam(CODE
LOWvggt/utils/helper.py33def create_pixel_coordinate_grid(num_frames, height, width):CODE
LOWvggt/models/aggregator.py291 def _process_global_attention(self, tokens, B, S, P, C, global_idx, pos=None):CODE
Self-Referential Comments8 hits · 26 pts
SeverityFileLineSnippetContext
MEDIUMdemo_viser.py126 # Create the main point cloud handleCOMMENT
MEDIUMdemo_gradio.py112 # Create a unique folder nameCOMMENT
MEDIUMvisual_util.py294 # Create an identity matrixCOMMENT
MEDIUMtraining/data/base_dataset.py290 # Create the valid range of indicesCOMMENT
MEDIUMtraining/data/composed_dataset.py254 # Create the tuple to pass to the underlying datasetCOMMENT
MEDIUMvggt/layers/rope.py9# This module provides a clean implementation of 2D Rotary Position Embeddings,COMMENT
MEDIUMvggt/utils/visual_track.py212 # Create a grid of imagesCOMMENT
MEDIUMvggt/utils/load_fn.py73 # Create a new black square image and paste originalCOMMENT
AI Structural Patterns22 hits · 22 pts
SeverityFileLineSnippetContext
LOWvisual_util.py18CODE
LOWvisual_util.py326CODE
LOWtraining/trainer.py60CODE
LOWvggt/layers/attention.py22CODE
LOWvggt/layers/vision_transformer.py43CODE
LOWvggt/layers/block.py28CODE
LOWvggt/layers/drop_path.py23CODE
LOWvggt/dependency/track_predict.py12CODE
LOWvggt/dependency/np_to_pycolmap.py12CODE
LOWvggt/dependency/track_modules/blocks.py160CODE
LOWvggt/dependency/track_modules/blocks.py307CODE
LOWvggt/dependency/track_modules/base_track_predictor.py16CODE
LOWvggt/utils/rotation.py103CODE
LOWvggt/heads/track_head.py18CODE
LOWvggt/heads/camera_head.py26CODE
LOWvggt/heads/dpt_head.py43CODE
LOWvggt/heads/dpt_head.py291CODE
LOWvggt/heads/dpt_head.py456CODE
LOWvggt/heads/track_modules/blocks.py24CODE
LOWvggt/heads/track_modules/blocks.py228CODE
LOWvggt/heads/track_modules/base_track_predictor.py18CODE
LOWvggt/models/aggregator.py52CODE
Deep Nesting17 hits · 17 pts
SeverityFileLineSnippetContext
LOWvisual_util.py18CODE
LOWtraining/trainer.py838CODE
LOWtraining/trainer.py501CODE
LOWtraining/train_utils/gradient_clip.py40CODE
LOWtraining/train_utils/general.py137CODE
LOWtraining/data/dynamic_dataloader.py151CODE
LOWtraining/data/track_util.py335CODE
LOWtraining/data/datasets/co3d.py68CODE
LOWvggt/dependency/vggsfm_utils.py189CODE
LOWvggt/dependency/np_to_pycolmap.py12CODE
LOWvggt/dependency/track_modules/modules.py44CODE
LOWvggt/dependency/track_modules/blocks.py26CODE
LOWvggt/dependency/track_modules/blocks.py91CODE
LOWvggt/heads/head_act.py38CODE
LOWvggt/heads/head_act.py61CODE
LOWvggt/heads/track_modules/modules.py44CODE
LOWvggt/models/aggregator.py187CODE
Cross-File Repetition3 hits · 15 pts
SeverityFileLineSnippetContext
HIGHvggt/dependency/track_modules/utils.py0this function generates a 1d positional embedding from a given grid using sine and cosine functions. args: - embed_dim: STRING
HIGHvggt/heads/utils.py0this function generates a 1d positional embedding from a given grid using sine and cosine functions. args: - embed_dim: STRING
HIGHvggt/heads/track_modules/utils.py0this function generates a 1d positional embedding from a given grid using sine and cosine functions. args: - embed_dim: STRING
Verbosity Indicators4 hits · 9 pts
SeverityFileLineSnippetContext
LOWvggt/dependency/projection.py127 # Step 1: Apply extrinsic parametersCOMMENT
LOWvggt/dependency/projection.py134 # Step 2: Apply intrinsic parameters and (optional) distortionCOMMENT
LOWvggt/utils/geometry.py235 # Step 1: Apply extrinsic parametersCOMMENT
LOWvggt/utils/geometry.py244 # Step 2: Apply intrinsic parameters and (optional) distortionCOMMENT
Structural Annotation Overuse4 hits · 9 pts
SeverityFileLineSnippetContext
LOWvggt/dependency/projection.py127 # Step 1: Apply extrinsic parametersCOMMENT
LOWvggt/dependency/projection.py134 # Step 2: Apply intrinsic parameters and (optional) distortionCOMMENT
LOWvggt/utils/geometry.py235 # Step 1: Apply extrinsic parametersCOMMENT
LOWvggt/utils/geometry.py244 # Step 2: Apply intrinsic parameters and (optional) distortionCOMMENT
Synthetic Comment Markers1 hit · 8 pts
SeverityFileLineSnippetContext
HIGHdemo_gradio.py281 # If it's an example click, skip as requestedCOMMENT
Modern Structural Boilerplate4 hits · 5 pts
SeverityFileLineSnippetContext
LOWdemo_viser.py199 def update_point_cloud() -> None:CODE
LOWtraining/trainer.py175 def _setup_env_variables(self, env_variables_conf: Optional[Dict[str, Any]]) -> None:CODE
LOWtraining/trainer.py182 def _setup_torch_dist_and_backend(self, cuda_conf: Dict, distributed_conf: Dict) -> None:CODE
LOWvggt/models/aggregator.py19logger = logging.getLogger(__name__)CODE
Excessive Try-Catch Wrapping4 hits · 5 pts
SeverityFileLineSnippetContext
MEDIUMvisual_util.py457 print(f"Error downloading file: {e}")CODE
LOWtraining/loss.py638 except Exception:CODE
LOWtraining/train_utils/general.py208 except Exception as e:CODE
LOWtraining/data/datasets/vkitti.py133 except Exception as e:CODE
Redundant / Tautological Comments3 hits · 4 pts
SeverityFileLineSnippetContext
LOWvisual_util.py114 # Check if mask already existsCOMMENT
LOWvggt/utils/load_fn.py196 # Check if we have different shapesCOMMENT
LOWvggt/utils/geometry.py140 # Check if se3 is a numpy array or a torch tensorCOMMENT
AI Slop Vocabulary3 hits · 4 pts
SeverityFileLineSnippetContext
LOWtraining/loss.py583 # Too few elements, just return as-isCOMMENT
LOWvggt/dependency/track_modules/base_track_predictor.py128 # (In my trials, it is also okay to just add the flows_emb instead of concat)COMMENT
LOWvggt/heads/track_modules/base_track_predictor.py139 # (In my trials, it is also okay to just add the flows_emb instead of concat)COMMENT
Over-Commented Block4 hits · 4 pts
SeverityFileLineSnippetContext
LOWvggt/dependency/track_modules/track_refine.py61 # For example, if the query point xy is (128.16, 252.78),COMMENT
LOWvggt/dependency/track_modules/track_refine.py201 # but it takes too much memory.COMMENT
LOWvggt/dependency/track_modules/track_refine.py221 # for the detailed explanation of unfold()COMMENT
LOWvggt/heads/track_modules/__init__.py1# Copyright (c) Meta Platforms, Inc. and affiliates.COMMENT
Slop Phrases1 hit · 2 pts
SeverityFileLineSnippetContext
MEDIUMtraining/config/default.yaml8accum_steps: 2 # We did not use gradient accumulation in our training, while if you suffer from OOM, you can try to uCODE