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facebookresearch/vggt

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

30.1 High pattern signal View on GitHub

Maintenance and Governance Review

This report summarizes scanner evidence for facebookresearch/vggt, a Python project with 14,478 GitHub stars. SynthScan examined 16,113 lines of code across 81 source files, recording 289 pattern matches distributed across 18 syntactic categories. The legacy adjusted pattern signal is 30.1 (High 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.

30.1
Adjusted Signal
30.1
Raw Pattern Score
100%
Time Factor
2026-05-19
Last Push
14.5K
Stars
Python
Language
16.1K
Lines of Code
81
Files
289
Pattern Hits
2026-10-12
Scan Date
0.20
HC Hit Rate

Risk Review · v3

Use each vector to guide manual verification of the underlying findings.

Cognitive Ownership Risk 48.3/100 · MEDIUM

Can maintainers explain intent, assumptions, and trade-offs? 131 finding(s).

Hallucinated Dependency & Supply Chain Risk 26.8/100 · MEDIUM

Are imports declared and package/API names verified? 101 finding(s).

Agent Over-Engineering Ratio 53.4/100 · HIGH

Are wrappers, guards, and repeated structures necessary? 132 finding(s).

PR Review Velocity Impact 77.4/100 · CRITICAL

Which changes need focused human review? 289 finding(s).

What These Metrics Mean

Adjusted Signal
Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score
Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor
A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits
Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate
High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files
Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

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.

Severity Breakdown

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.

CRITICAL 0HIGH 16MEDIUM 55LOW 218

Directory Score Breakdown

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.

Pattern Findings

The scanner identified 289 distinct pattern matches across 18 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.

Decorative Section Separators45 hits · 142 pts
SeverityFileLineSnippetContext
MEDIUM⚡demo_gradio.py100# -------------------------------------------------------------------------COMMENT
MEDIUM⚡demo_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
MEDIUM⚡training/train_utils/optimizer.py29 # ---------------------------------------------------------------------COMMENT
MEDIUM⚡training/train_utils/optimizer.py31 # ---------------------------------------------------------------------COMMENT
MEDIUM⚡training/train_utils/optimizer.py59# -----------------------------------------------------------------------------COMMENT
MEDIUM⚡training/train_utils/optimizer.py61# -----------------------------------------------------------------------------COMMENT
MEDIUM⚡training/train_utils/optimizer.py222 # ──────────────────────────────────────────────────────────────────COMMENT
MEDIUM⚡training/train_utils/optimizer.py224 # ──────────────────────────────────────────────────────────────────COMMENT
MEDIUM⚡training/train_utils/optimizer.py229 # ──────────────────────────────────────────────────────────────────COMMENT
MEDIUM⚡training/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
MEDIUM⚡vggt/dependency/projection.py29 # 1. perspective divide ───────────────────────────────────────COMMENT
MEDIUM⚡vggt/dependency/projection.py34 # 2. optional distortion ──────────────────────────────────────COMMENT
MEDIUM⚡vggt/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
LOW⚡training/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
LOW⚡training/train_utils/optimizer.py41 def _validate_optimizer_schedulers(self):CODE
LOW⚡training/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
MEDIUM⚡demo_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
LOW⚡vggt/dependency/projection.py127 # Step 1: Apply extrinsic parametersCOMMENT
LOW⚡vggt/dependency/projection.py134 # Step 2: Apply intrinsic parameters and (optional) distortionCOMMENT
LOW⚡vggt/utils/geometry.py235 # Step 1: Apply extrinsic parametersCOMMENT
LOW⚡vggt/utils/geometry.py244 # Step 2: Apply intrinsic parameters and (optional) distortionCOMMENT
Structural Annotation Overuse4 hits · 9 pts
SeverityFileLineSnippetContext
LOW⚡vggt/dependency/projection.py127 # Step 1: Apply extrinsic parametersCOMMENT
LOW⚡vggt/dependency/projection.py134 # Step 2: Apply intrinsic parameters and (optional) distortionCOMMENT
LOW⚡vggt/utils/geometry.py235 # Step 1: Apply extrinsic parametersCOMMENT
LOW⚡vggt/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
LOW⚡training/trainer.py175 def _setup_env_variables(self, env_variables_conf: Optional[Dict[str, Any]]) -> None:CODE
LOW⚡training/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
Empty Error Handlers1 hit · 5 pts
SeverityFileLineSnippetContext
HIGHvisual_util.py65except (ValueError, IndexError):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