Ultralytics YOLOv3 in PyTorch > ONNX > CoreML > TFLite
This report presents the forensic synthetic code analysis of ultralytics/yolov3, a Python project with 10,580 GitHub stars. SynthScan v2.0 examined 15,270 lines of code across 76 source files, recording 160 pattern matches distributed across 14 syntactic categories. The overall adjusted score of 19.7 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).
Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.
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 160 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 |
|---|---|---|---|---|
| HIGH | val.py | 67 | Saves detection results in a text format, including labels and optionally confidence scores. Args: predn (t | STRING |
| HIGH | val.py | 109 | Save detection results in JSON format containing image_id, category_id, bbox, and score per detection. Args: | STRING |
| HIGH | val.py | 150 | Computes correct prediction matrix for detections against ground truth labels at various IoU thresholds. Args: | STRING |
| HIGH | val.py | 224 | Validate a trained YOLOv3 detection model on a dataset and optionally save results in the requested formats. Args: | STRING |
| HIGH | val.py | 493 | Parses and returns command-line options for dataset paths, model parameters, and inference settings. Args: | STRING |
| HIGH | val.py | 566 | Executes model tasks including training, validation, and speed or study benchmarks based on specified options. Args | STRING |
| HIGH | export.py | 168 | Export a YOLOv3 model to TorchScript format, with optional optimization for mobile deployment. Args: model | STRING |
| HIGH | export.py | 200 | Export a YOLOv3 model to ONNX format with dynamic shape and simplification options. Args: model (torch.nn.M | STRING |
| HIGH | export.py | 272 | Export a YOLOv3 model to OpenVINO format with optional INT8 quantization and inference metadata. Args: file | STRING |
| HIGH | export.py | 353 | Export a YOLOv3 model to PaddlePaddle format using X2Paddle, writing model files and a metadata YAML. Args: | STRING |
| HIGH | export.py | 384 | Export a YOLOv3 model to CoreML format with optional quantization and Non-Maximum Suppression (NMS). Args: | STRING |
| HIGH | export.py | 429 | Export a YOLOv3 model to TensorRT engine format, optimizing it for GPU inference. Args: model (torch.nn.Mod | STRING |
| HIGH | export.py | 678 | Export a YOLOv3 PyTorch model to one or more deployment formats (TorchScript, ONNX, CoreML, TensorRT, etc.). Args: | STRING |
| HIGH | train.py | 109 | Train a YOLOv3 model on a custom dataset and manage the training process. Args: hyp (str | dict): Path to h | STRING |
| HIGH | train.py | 538 | Parse command line arguments for configuring the training of a YOLOv3 model. Args: known (bool): Flag to pa | STRING |
| HIGH | train.py | 607 | Main training/evolution script handling model checks, DDP setup, training, and hyperparameter evolution. Args: | STRING |
| HIGH | train.py | 804 | Run the training process for a YOLOv3 model with the specified configurations. Args: data (str): Path to th | STRING |
| HIGH | hubconf.py | 17 | Creates or loads a YOLOv3 model with specified configurations and optional pretrained weights. Args: name ( | STRING |
| HIGH | hubconf.py | 87 | Loads a custom or local YOLOv3 model from a specified path, with options for autoshaping and device assignment. Arg | STRING |
| HIGH | hubconf.py | 116 | Instantiates the YOLOv3 model with optional pretrained weights, configurable input channels, classes, autoshaping, a | STRING |
| HIGH | hubconf.py | 145 | Instantiates the YOLOv3-SPP model with optional pretrained weights, configurable input channels, classes, autoshapin | STRING |
| HIGH | hubconf.py | 171 | Instantiates the YOLOv3-tiny model with optional pretrained weights, configurable input channels, classes, autoshapi | STRING |
| HIGH | detect.py | 99 | Run YOLOv3 detection inference on various input sources such as images, videos, and streams. Args: weights | STRING |
| HIGH | detect.py | 290 | Parses and returns command-line options for running YOLOv3 model detection. Args: --weights (list[str]): Mo | STRING |
| HIGH | detect.py | 367 | Entry point for running the YOLOv3 model; checks requirements and calls `run` with parsed options. Args: op | STRING |
| HIGH | benchmarks.py | 58 | Run YOLOv3 benchmarks on multiple export formats and validate performance metrics. Args: weights (str | Pat | STRING |
| HIGH | benchmarks.py | 177 | Parses command line arguments for YOLOv3 inference and export configurations. Args: --weights (str): Path t | STRING |
| HIGH | benchmarks.py | 222 | Executes the export and benchmarking pipeline for YOLOv3 models, testing multiple export formats and validating perf | STRING |
| HIGH | utils/autoanchor.py | 69 | Create k-means evolved anchors from a training dataset. Args: dataset (str | LoadImagesAndLabels): Path to | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 478 | except Exception as e: | CODE |
| LOW | export.py | 159 | except Exception as e: | CODE |
| LOW | export.py | 265 | except Exception as e: | CODE |
| LOW | export.py | 456 | except Exception: | CODE |
| LOW | hubconf.py | 64 | except Exception: | CODE |
| LOW | hubconf.py | 80 | except Exception as e: | CODE |
| LOW | benchmarks.py | 103 | except Exception as e: | CODE |
| LOW | benchmarks.py | 164 | except Exception: | CODE |
| LOW | utils/autobatch.py | 64 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 541 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 551 | except Exception: | CODE |
| LOW | utils/dataloaders.py | 704 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 1118 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 1147 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 1191 | except Exception as e: # use OpenCV | CODE |
| LOW | utils/downloads.py | 20 | except Exception: | CODE |
| LOW | utils/downloads.py | 66 | except Exception as e: # url2 | CODE |
| LOW⚡ | utils/downloads.py | 109 | except Exception: | CODE |
| LOW⚡ | utils/downloads.py | 112 | except Exception: | CODE |
| LOW⚡ | utils/downloads.py | 115 | except Exception: | CODE |
| LOW | utils/plots.py | 398 | except Exception as e: | CODE |
| LOW | utils/plots.py | 432 | except Exception as e: | CODE |
| LOW | utils/autoanchor.py | 144 | except Exception: | CODE |
| LOW | utils/torch_utils.py | 148 | except Exception: | CODE |
| LOW | utils/torch_utils.py | 159 | except Exception: # no backward method | CODE |
| LOW | utils/torch_utils.py | 169 | except Exception as e: | CODE |
| LOW | utils/torch_utils.py | 229 | except Exception: | CODE |
| LOW | utils/general.py | 337 | except Exception: | CODE |
| LOW | utils/general.py | 427 | except Exception as e: | CODE |
| LOW | utils/general.py | 574 | except Exception: | CODE |
| LOW | utils/general.py | 1134 | except Exception: | CODE |
| LOW | utils/augmentations.py | 41 | except Exception as e: | CODE |
| LOW | utils/loggers/__init__.py | 118 | except Exception: | CODE |
| LOW | utils/loggers/__init__.py | 421 | except Exception as e: | CODE |
| LOW | utils/loggers/comet/comet_utils.py | 65 | except Exception as e: | CODE |
| LOW | models/yolo.py | 413 | except Exception as e: | CODE |
| MEDIUM | models/yolo.py | 414 | print(f"Error in {cfg}: {e}") | CODE |
| LOW | .github/workflows/merge-main-into-prs.yml | 74 | except Exception as update_error: | CODE |
| LOW | .github/workflows/merge-main-into-prs.yml | 80 | except Exception as e: | CODE |
| LOW | data/Objects365.yaml | 439 | except Exception as e: | CODE |
| LOW | data/xView.yaml | 134 | except Exception as e: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 194 | CODE | |
| LOW | val.py | 565 | CODE | |
| LOW | train.py | 108 | CODE | |
| LOW | train.py | 606 | CODE | |
| LOW | hubconf.py | 16 | CODE | |
| LOW | detect.py | 70 | CODE | |
| LOW | utils/metrics.py | 235 | CODE | |
| LOW | utils/dataloaders.py | 1010 | CODE | |
| LOW | utils/dataloaders.py | 1073 | CODE | |
| LOW | utils/dataloaders.py | 282 | CODE | |
| LOW | utils/dataloaders.py | 495 | CODE | |
| LOW | utils/dataloaders.py | 823 | CODE | |
| LOW | utils/dataloaders.py | 883 | CODE | |
| LOW | utils/downloads.py | 81 | CODE | |
| LOW | utils/plots.py | 145 | CODE | |
| LOW | utils/plots.py | 405 | CODE | |
| LOW | utils/torch_utils.py | 122 | CODE | |
| LOW | utils/torch_utils.py | 236 | CODE | |
| LOW | utils/general.py | 449 | CODE | |
| LOW | utils/general.py | 491 | CODE | |
| LOW | utils/general.py | 612 | CODE | |
| LOW | utils/general.py | 615 | CODE | |
| LOW | models/common.py | 434 | CODE | |
| LOW | models/common.py | 641 | CODE | |
| LOW | models/common.py | 806 | CODE | |
| LOW | models/common.py | 898 | CODE | |
| LOW | models/yolo.py | 300 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | requirements.txt | 21 | COMMENT | |
| LOW | requirements.txt | 41 | setuptools>=70.0.0 # Required to avoid known vulnerabilities | COMMENT |
| LOW | pyproject.toml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | export.py | 561 | # from coremltools.models.neural_network import flexible_shape_utils | COMMENT |
| LOW | utils/autoanchor.py | 141 | s = wh.std(0) # sigmas for whitening | COMMENT |
| LOW | models/common.py | 441 | # TorchScript: *.torchscript | COMMENT |
| LOW | data/coco128.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/coco.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/GlobalWheat2020.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/VisDrone.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/SKU-110K.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/Objects365.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/xView.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/Argoverse.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/VOC.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | data/hyps/hyp.VOC.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 431 | # Print results | COMMENT |
| LOW | val.py | 437 | # Print results per class | COMMENT |
| LOW | detect.py | 227 | # Print results | COMMENT |
| LOW | detect.py | 279 | # Print results | COMMENT |
| LOW | benchmarks.py | 111 | # Print results | COMMENT |
| LOW | benchmarks.py | 167 | # Print results | COMMENT |
| LOW | utils/loggers/comet/__init__.py | 163 | # Check if running the Experiment with the Comet Optimizer | COMMENT |
| LOW | utils/loggers/comet/__init__.py | 465 | # Check if running Experiment with Comet Optimizer | COMMENT |
| LOW | utils/loggers/clearml/clearml_utils.py | 118 | # Set data to data_dict because wandb will crash without this information and opt is the best way | COMMENT |
| LOW | .github/workflows/merge-main-into-prs.yml | 62 | # Check if PR is behind the default branch | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils/triton.py | 4 | CODE | |
| LOW | utils/torch_utils.py | 15 | CODE | |
| LOW | utils/torch_utils.py | 16 | CODE | |
| LOW | utils/torch_utils.py | 17 | CODE | |
| LOW | utils/torch_utils.py | 18 | CODE | |
| LOW | utils/torch_utils.py | 19 | CODE | |
| LOW | utils/general.py | 4 | CODE | |
| LOW | utils/general.py | 37 | CODE | |
| LOW | utils/general.py | 38 | CODE | |
| LOW | utils/general.py | 43 | CODE | |
| LOW | utils/general.py | 45 | CODE | |
| LOW | utils/general.py | 47 | CODE | |
| LOW | utils/general.py | 48 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| CRITICAL | export.py | 637 | pipeline.spec.description.metadata.userDefined.update( | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 194 | CODE | |
| LOW | export.py | 660 | CODE | |
| LOW | detect.py | 70 | CODE | |
| LOW | benchmarks.py | 47 | CODE | |
| LOW | benchmarks.py | 125 | CODE | |
| LOW | utils/dataloaders.py | 126 | CODE | |
| LOW | utils/dataloaders.py | 495 | CODE | |
| LOW | utils/general.py | 876 | CODE | |
| LOW | utils/augmentations.py | 132 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils/triton.py | 34 | def create_input_placeholders() -> list[InferInput]: | CODE |
| LOW | utils/triton.py | 47 | def create_input_placeholders() -> list[InferInput]: | CODE |
| LOW | utils/torch_utils.py | 75 | def torch_distributed_zero_first(local_rank: int): | CODE |
| LOW | utils/loggers/__init__.py | 159 | def on_pretrain_routine_start(self): | CODE |
| LOW | utils/loggers/comet/__init__.py | 372 | def download_dataset_artifact(self, artifact_path): | CODE |
| LOW | utils/loggers/comet/comet_utils.py | 22 | def download_model_checkpoint(opt, experiment): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | utils/loggers/comet/README.md | 47 | api_key=YOUR_API_KEY | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | pyproject.toml | 19 | # For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | utils/general.py | 1112 | # ------------------------------------------------------------------------------------ | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils/loggers/comet/__init__.py | 12 | logger = logging.getLogger(__name__) | CODE |
| LOW | utils/loggers/comet/comet_utils.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | utils/loggers/comet/hpo.py | 15 | logger = logging.getLogger(__name__) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | train.py | 126 | # Example usage | STRING |
| LOW | utils/autobatch.py | 32 | # Usage: | COMMENT |