The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
This report presents the forensic synthetic code analysis of facebookresearch/sam2, a Jupyter Notebook project with 19,520 GitHub stars. SynthScan v2.0 examined 40,255 lines of code across 294 source files, recording 221 pattern matches distributed across 14 syntactic categories. The overall adjusted score of 7.2 places this repository in the Low 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).
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.
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.
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.
The scanner identified 221 distinct pattern matches across 14 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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …mo/frontend/src/common/loading/UploadLoadingScreen.tsx | 49 | function UploadLoadingScreenChangeVideoTrigger({ | CODE |
| LOW | demo/frontend/src/common/utils/ImageUtils.ts | 16 | export function convertVideoFrameToImageData( | CODE |
| LOW | …ntend/src/common/components/MobileFirstClickBanner.tsx | 62 | function MobileVideoGalleryModalTrigger({ | CODE |
| LOW | …components/options/MoreOptionsToolbarBottomActions.tsx | 29 | export default function MoreOptionsToolbarBottomActions({onTabChange}: Props) { | CODE |
| LOW | …common/components/video/VideoFilmstripWithPlayback.tsx | 41 | export default function VideoFilmstripWithPlayback() { | CODE |
| LOW | …ntend/src/common/components/video/editor/ImageUtils.ts | 16 | export function convertVideoFrameToImageData( | CODE |
| LOW | …src/common/components/video/editor/DemoVideoEditor.tsx | 194 | async function handleOptimisticPointUpdate(newPoints: SegmentationPoint[]) { | CODE |
| LOW | …components/gallery/DefaultVideoGalleryModalTrigger.tsx | 20 | export default function DefaultVideoGalleryModalTrigger({ | CODE |
| LOW | …rc/common/components/gallery/DemoVideoGalleryModal.tsx | 122 | function handleOpenVideoGalleryModal() { | CODE |
| LOW | …common/components/toolbar/useListenToStreamingState.ts | 23 | export default function useListenToStreamingState(): { | CODE |
| LOW | …mon/components/toolbar/ToolbarBottomActionsWrapper.tsx | 34 | export default function ToolbarBottomActionsWrapper({ | CODE |
| LOW | …components/annotations/ClearAllPointsInVideoButton.tsx | 37 | export default function ClearAllPointsInVideoButton({onRestart}: Props) { | CODE |
| LOW | …components/annotations/ObjectsToolbarBottomActions.tsx | 31 | export default function ObjectsToolbarBottomActions({onTabChange}: Props) { | CODE |
| LOW | …/components/annotations/MobileObjectsToolbarHeader.tsx | 20 | export default function MobileObjectsToolbarHeader() { | CODE |
| LOW | …mon/components/effects/EffectsToolbarBottomActions.tsx | 29 | export default function EffectsToolbarBottomActions({onTabChange}: Props) { | CODE |
| LOW | …mon/components/effects/EffectsToolbarBottomActions.tsx | 30 | function handleSwitchToMoreOptionsTab() { | CODE |
| LOW | …mmon/components/session/useCloseSessionBeforeUnload.ts | 32 | export default function useCloseSessionBeforeUnload() { | CODE |
| LOW | …o/frontend/src/common/error/ErrorSerializationUtils.ts | 21 | export function registerSerializableConstructors() { | CODE |
| LOW | demo/frontend/src/graphql/RelayEnvironmentProvider.tsx | 36 | export default function OnevisionRelayEnvironmentProvider({ | CODE |
| LOW | demo/backend/server/app.py | 90 | def gen_track_with_mask_stream( | CODE |
| LOW | demo/backend/server/inference/predictor.py | 357 | def cancel_propagate_in_video( | CODE |
| LOW | demo/backend/server/data/schema.py | 246 | def cancel_propagate_in_video( | CODE |
| LOW | demo/backend/server/data/schema.py | 269 | def _get_start_sec_duration_sec( | CODE |
| LOW | tools/vos_inference.py | 249 | def vos_separate_inference_per_object( | CODE |
| LOW | sav_dataset/utils/sav_utils.py | 101 | def get_frames_and_annotations( | CODE |
| LOW | training/train.py | 117 | def add_pythonpath_to_sys_path(): | CODE |
| LOW | training/optimizer.py | 41 | def _validate_optimizer_schedulers(self): | CODE |
| LOW | training/optimizer.py | 112 | def name_constraints_to_parameters( | CODE |
| LOW | training/optimizer.py | 131 | def map_scheduler_cfgs_to_param_groups( | CODE |
| LOW | training/optimizer.py | 172 | def validate_param_group_params(param_groups: List[Dict], model: nn.Module): | CODE |
| LOW | training/optimizer.py | 194 | def unix_module_cls_pattern_to_parameter_names( | CODE |
| LOW | training/optimizer.py | 227 | def unix_param_pattern_to_parameter_names( | CODE |
| LOW | training/optimizer.py | 253 | def _unix_pattern_to_parameter_names( | CODE |
| LOW | training/optimizer.py | 275 | def get_module_cls_to_param_names( | CODE |
| LOW | training/optimizer.py | 422 | def layer_decay_param_modifier( | CODE |
| LOW⚡ | training/trainer.py | 253 | def _infer_distributed_backend_if_none(self, distributed_conf, accelerator): | CODE |
| LOW⚡ | training/trainer.py | 262 | def _setup_torch_dist_and_backend(self, cuda_conf, distributed_conf) -> None: | CODE |
| LOW | training/trainer.py | 291 | def _setup_ddp_distributed_training(self, distributed_conf, accelerator): | CODE |
| LOW | training/trainer.py | 422 | def _load_resuming_checkpoint(self, ckpt_path: str): | CODE |
| LOW | training/trainer.py | 447 | def is_intermediate_val_epoch(self, epoch): | CODE |
| LOW | training/trainer.py | 891 | def _log_meters_and_save_best_ckpts(self, phases: List[str]): | CODE |
| LOW | training/trainer.py | 1033 | def _log_loss_detailed_and_return_core_loss(self, loss, loss_str, step): | CODE |
| LOW | training/dataset/transforms.py | 37 | def get_size_with_aspect_ratio(image_size, size, max_size=None): | CODE |
| LOW | training/utils/train_utils.py | 52 | def register_omegaconf_resolvers(): | CODE |
| LOW | training/utils/train_utils.py | 66 | def setup_distributed_backend(backend, timeout_mins): | CODE |
| LOW | training/utils/train_utils.py | 81 | def get_machine_local_and_dist_rank(): | CODE |
| LOW | training/utils/train_utils.py | 130 | def is_dist_avail_and_initialized(): | CODE |
| LOW | training/utils/distributed.py | 186 | def convert_to_distributed_tensor(tensor: torch.Tensor) -> Tuple[torch.Tensor, str]: | CODE |
| LOW | training/utils/distributed.py | 212 | def is_distributed_training_run() -> bool: | CODE |
| LOW⚡ | training/utils/distributed.py | 383 | def init_distributed_data_parallel_model( | CODE |
| LOW | training/utils/distributed.py | 504 | def all_gather_batch_with_grad(tensors): | CODE |
| LOW | training/utils/distributed.py | 571 | def is_dist_avail_and_initialized(): | CODE |
| LOW | training/utils/checkpoint_utils.py | 30 | def unix_pattern_to_parameter_names( | CODE |
| LOW | training/utils/checkpoint_utils.py | 47 | def filter_params_matching_unix_pattern( | CODE |
| LOW | training/utils/checkpoint_utils.py | 68 | def exclude_params_matching_unix_pattern( | CODE |
| LOW | training/utils/checkpoint_utils.py | 99 | def assert_skipped_parameters_are_frozen(model: nn.Module, patterns: List[str]): | CODE |
| LOW | training/utils/checkpoint_utils.py | 123 | def with_check_parameter_frozen( | CODE |
| LOW | training/utils/checkpoint_utils.py | 247 | def load_checkpoint_and_apply_kernels( | STRING |
| LOW | training/utils/checkpoint_utils.py | 297 | def check_load_state_dict_errors( | STRING |
| LOW | training/utils/checkpoint_utils.py | 330 | def load_state_dict_into_model( | STRING |
| 27 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | demo/backend/server/inference/predictor.py | 270 | CODE | |
| LOW | tools/vos_inference.py | 249 | CODE | |
| LOW | sav_dataset/utils/sav_benchmark.py | 156 | CODE | |
| LOW | sav_dataset/utils/sav_benchmark.py | 316 | CODE | |
| LOW | sav_dataset/utils/sav_benchmark.py | 237 | CODE | |
| LOW | training/optimizer.py | 422 | CODE | |
| LOW | training/optimizer.py | 52 | CODE | |
| LOW | training/trainer.py | 583 | CODE | |
| LOW | training/trainer.py | 703 | CODE | |
| LOW | training/trainer.py | 891 | CODE | |
| LOW | training/dataset/transforms.py | 104 | CODE | |
| LOW | training/dataset/transforms.py | 272 | CODE | |
| LOW | training/dataset/transforms.py | 354 | CODE | |
| LOW | training/dataset/vos_dataset.py | 79 | CODE | |
| LOW | training/dataset/vos_sampler.py | 42 | CODE | |
| LOW | training/dataset/vos_segment_loader.py | 24 | CODE | |
| LOW | training/utils/train_utils.py | 29 | CODE | |
| LOW | sam2/sam2_video_predictor_legacy.py | 426 | CODE | |
| LOW | sam2/sam2_video_predictor.py | 480 | CODE | |
| LOW | sam2/sam2_video_predictor.py | 546 | CODE | |
| LOW | sam2/utils/amg.py | 46 | CODE | |
| LOW | sam2/utils/amg.py | 61 | CODE | |
| LOW | sam2/modeling/sam2_base.py | 497 | CODE | |
| LOW | sam2/modeling/backbones/hieradet.py | 301 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | setup.py | 104 | except Exception as e: | CODE |
| LOW⚡ | setup.py | 122 | except Exception as e: | CODE |
| LOW⚡ | setup.py | 129 | except Exception as e: | CODE |
| LOW⚡ | setup.py | 136 | except Exception as e: | CODE |
| LOW | setup.py | 148 | except Exception as e: | CODE |
| MEDIUM | setup.py | 119 | def finalize_options(self): | CODE |
| MEDIUM | setup.py | 126 | def build_extensions(self): | CODE |
| MEDIUM | setup.py | 133 | def get_ext_filename(self, ext_name): | CODE |
| LOW | training/train.py | 37 | except Exception as e: | CODE |
| LOW | training/train.py | 99 | except Exception as e: | CODE |
| LOW | training/dataset/vos_dataset.py | 64 | except Exception as e: | CODE |
| LOW | training/dataset/sam2_datasets.py | 61 | except Exception as e: | CODE |
| LOW | sam2/utils/transforms.py | 105 | except Exception as e: | CODE |
| LOW | sam2/utils/misc.py | 141 | except Exception as e: | CODE |
| LOW | sam2/utils/misc.py | 326 | except Exception as e: | CODE |
| MEDIUM | sam2/utils/misc.py | 137 | def _load_frames(): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | sam2/automatic_mask_generator.py | 0 | load a pretrained model from the hugging face hub. arguments: model_id (str): the hugging face repository id. **kwargs: | STRING |
| HIGH | sam2/sam2_video_predictor_legacy.py | 0 | load a pretrained model from the hugging face hub. arguments: model_id (str): the hugging face repository id. **kwargs: | STRING |
| HIGH | sam2/sam2_video_predictor.py | 0 | load a pretrained model from the hugging face hub. arguments: model_id (str): the hugging face repository id. **kwargs: | STRING |
| HIGH | sam2/sam2_image_predictor.py | 0 | load a pretrained model from the hugging face hub. arguments: model_id (str): the hugging face repository id. **kwargs: | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/dataset/transforms.py | 423 | # Step 1: downsize the images and paste them into a mosaic | COMMENT |
| LOW | training/dataset/transforms.py | 467 | # Step 2: downsize the masks and paste them into the target grid of the mosaic | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1065 | # Step 0: clear the input on those frames where this object id has point or mask input | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1080 | # Step 1: Update the object id mapping (note that it must be done after Step 0, | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1094 | # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys. | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1110 | # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices. | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1132 | # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 890 | # Step 0: clear the input on those frames where this object id has point or mask input | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 905 | # Step 1: Update the object id mapping (note that it must be done after Step 0, | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 919 | # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys. | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 934 | # Step 3: Further collect the outputs on those frames in `obj_input_frames_inds`, which | COMMENT |
| LOW | sam2/modeling/sam2_base.py | 521 | # Step 1: condition the visual features of the current frame on previous memories | COMMENT |
| LOW | sam2/modeling/sam2_base.py | 663 | # Step 2: Concatenate the memories and forward through the transformer encoder | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/dataset/transforms.py | 423 | # Step 1: downsize the images and paste them into a mosaic | COMMENT |
| LOW | training/dataset/transforms.py | 467 | # Step 2: downsize the masks and paste them into the target grid of the mosaic | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1065 | # Step 0: clear the input on those frames where this object id has point or mask input | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1080 | # Step 1: Update the object id mapping (note that it must be done after Step 0, | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1094 | # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys. | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1110 | # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices. | COMMENT |
| LOW | sam2/sam2_video_predictor_legacy.py | 1132 | # Step 4: Further collect the outputs on those frames in `obj_input_frames_inds`, which | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 890 | # Step 0: clear the input on those frames where this object id has point or mask input | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 905 | # Step 1: Update the object id mapping (note that it must be done after Step 0, | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 919 | # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys. | COMMENT |
| LOW | sam2/sam2_video_predictor.py | 934 | # Step 3: Further collect the outputs on those frames in `obj_input_frames_inds`, which | COMMENT |
| LOW | sam2/modeling/sam2_base.py | 521 | # Step 1: condition the visual features of the current frame on previous memories | COMMENT |
| LOW | sam2/modeling/sam2_base.py | 663 | # Step 2: Concatenate the memories and forward through the transformer encoder | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | training/scripts/sav_frame_extraction_submitit.py | 20 | # ------------ | COMMENT |
| MEDIUM | training/scripts/sav_frame_extraction_submitit.py | 22 | # ------------ | COMMENT |
| MEDIUM | training/scripts/sav_frame_extraction_submitit.py | 40 | # ------------ | COMMENT |
| MEDIUM | training/scripts/sav_frame_extraction_submitit.py | 42 | # ------------ | COMMENT |
| MEDIUM | training/scripts/sav_frame_extraction_submitit.py | 64 | # ------------ | COMMENT |
| MEDIUM | training/scripts/sav_frame_extraction_submitit.py | 66 | # ------------ | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …ntend/src/common/components/video/VideoWorkerBridge.ts | 501 | // listener: (ev: WorkerEventMap[K]) => unknown, | COMMENT |
| LOW | …ntend/src/common/components/video/VideoWorkerBridge.ts | 521 | // } | COMMENT |
| LOW | training/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| LOW | training/dataset/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| LOW | training/utils/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| LOW | training/model/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| LOW | training/model/sam2.py | 41 | # - if `rand_init_cond_frames` below is True, we randomly sample 1~num_init_cond_frames initial conditioning fra | COMMENT |
| LOW | checkpoints/download_ckpts.sh | 21 | # sam2_hiera_t_url="${SAM2_BASE_URL}/sam2_hiera_tiny.pt" | COMMENT |
| LOW | sam2/utils/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| LOW | sam2/modeling/sam2_base.py | 41 | # (instead of using the transformer encoder) | COMMENT |
| LOW | sam2/modeling/sam2_base.py | 61 | memory_temporal_stride_for_eval=1, | COMMENT |
| LOW | sam2/modeling/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| LOW | sam2/modeling/sam/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| LOW | sam2/modeling/backbones/__init__.py | 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | demo/backend/server/app.py | 22 | CODE | |
| LOW | demo/backend/server/data/data_types.py | 6 | CODE | |
| LOW | demo/backend/server/data/data_types.py | 12 | CODE | |
| LOW | demo/backend/server/data/schema.py | 10 | CODE | |
| LOW | training/optimizer.py | 11 | CODE | |
| LOW | training/dataset/vos_raw_dataset.py | 8 | CODE | |
| LOW | training/dataset/vos_raw_dataset.py | 14 | CODE | |
| LOW | training/dataset/sam2_datasets.py | 9 | CODE | |
| LOW | training/utils/data_utils.py | 14 | CODE | |
| LOW | training/model/sam2.py | 13 | CODE | |
| LOW | training/model/sam2.py | 13 | CODE | |
| LOW | sam2/automatic_mask_generator.py | 127 | CODE | |
| LOW | sam2/sam2_video_predictor.py | 7 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | training/dataset/transforms.py | 274 | # Create a color jitter transformation params | COMMENT |
| MEDIUM | training/dataset/transforms.py | 359 | # Create a random affine transformation | COMMENT |
| MEDIUM | checkpoints/download_ckpts.sh | 19 | # Define the URLs for SAM 2 checkpoints | COMMENT |
| MEDIUM | checkpoints/download_ckpts.sh | 39 | # Define the URLs for SAM 2.1 checkpoints | COMMENT |
| MEDIUM | sam2/sam2_image_predictor.py | 185 | """This function is very similar to predict(...), however it is used for batched mode, when the model is expecte | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | demo/backend/server/app_conf.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | demo/backend/server/app.py | 27 | logger = logging.getLogger(__name__) | CODE |
| LOW | demo/backend/server/inference/predictor.py | 40 | logger = logging.getLogger(__name__) | CODE |
| LOW | demo/backend/server/data/store.py | 13 | def set_videos(videos: Dict[str, Video]) -> None: | CODE |
| LOW⚡ | training/trainer.py | 257 | def _setup_env_variables(self, env_variables_conf) -> None: | CODE |
| LOW⚡ | training/trainer.py | 262 | def _setup_torch_dist_and_backend(self, cuda_conf, distributed_conf) -> None: | CODE |
| LOW | training/dataset/sam2_datasets.py | 129 | def _set_dataset_epoch(self, dataset, epoch: int) -> None: | CODE |
| LOW⚡ | training/utils/distributed.py | 368 | def set_cuda_device_index(idx: int) -> None: | CODE |
| LOW⚡ | training/utils/distributed.py | 374 | def set_cpu_device() -> None: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | training/optimizer.py | 115 | Return parameters which match the intersection of parameter constraints. Note that this returns the parameters them | STRING |
| HIGH | training/optimizer.py | 135 | Produce parameter groups corresponding to all the scheduler configs. Takes all the scheduler configs, each of which | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/trainer.py | 958 | # Check if there are any duplicates | COMMENT |
| LOW | training/dataset/vos_raw_dataset.py | 90 | # Check if it's not in excluded_files | COMMENT |
| LOW | training/dataset/vos_raw_dataset.py | 182 | # Check if it's not in excluded_files and it exists | COMMENT |
| LOW | sam2/build_sam.py | 17 | # Check if the user is running Python from the parent directory of the sam2 repo | COMMENT |
| LOW | sam2/utils/amg.py | 242 | # Check if boxes has a channel dimension | COMMENT |
| LOW | sam2/utils/amg.py | 251 | # Check if points has a channel dimension | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/trainer.py | 148 | CODE | |
| LOW | training/model/sam2.py | 26 | CODE | |
| LOW | sam2/automatic_mask_generator.py | 37 | CODE | |
| LOW | sam2/modeling/sam2_base.py | 23 | CODE | |
| LOW | sam2/modeling/sam/transformer.py | 248 | CODE | |
| LOW | sam2/modeling/sam/transformer.py | 311 | CODE | |
| LOW | sam2/modeling/sam/mask_decoder.py | 16 | CODE | |
| LOW | sam2/modeling/backbones/image_encoder.py | 42 | CODE | |
| LOW | sam2/modeling/backbones/hieradet.py | 174 | CODE |