Ultralytics YOLOv5 in PyTorch > ONNX > CoreML > TFLite
This report presents the forensic synthetic code analysis of ultralytics/yolov5, a Python project with 57,678 GitHub stars. SynthScan v2.0 examined 25,874 lines of code across 131 source files, recording 229 pattern matches distributed across 15 syntactic categories. The overall adjusted score of 17.3 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).
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 229 distinct pattern matches across 15 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 a single JSON detection result, including image ID, category ID, bounding box, and confidence score. Args: | STRING |
| HIGH | val.py | 396 | Parse command-line options for configuring YOLOv5 model inference. Args: data (str, optional): Path to the | STRING |
| HIGH | val.py | 474 | Executes YOLOv5 tasks like training, validation, testing, speed, and study benchmarks based on provided options. Ar | STRING |
| HIGH | export.py | 97 | Initializes an iOS compatible model with normalization based on image dimensions. Args: model (torc | STRING |
| HIGH | export.py | 123 | Run a forward pass on the input tensor, returning class confidences and normalized coordinates. Args: | STRING |
| HIGH | export.py | 144 | Returns a DataFrame of supported YOLOv5 model export formats and their properties. Returns: pandas.DataFram | STRING |
| HIGH | export.py | 183 | Log success or failure, execution time, and file size for YOLOv5 model export functions wrapped with @try_export. A | STRING |
| HIGH | export.py | 226 | Export a YOLOv5 model to the TorchScript format. Args: model (torch.nn.Module): The YOLOv5 model to be expo | STRING |
| HIGH | export.py | 281 | Export a YOLOv5 model to ONNX format with dynamic axes support and optional model simplification. Args: mod | STRING |
| HIGH | export.py | 380 | Export a YOLOv5 model to OpenVINO format with optional FP16 and INT8 quantization. Args: file (Path): Path | STRING |
| HIGH | export.py | 464 | Export a YOLOv5 PyTorch model to PaddlePaddle format using X2Paddle, saving the converted model and metadata. Args: | STRING |
| HIGH | export.py | 512 | Export a YOLOv5 model to CoreML format with optional NMS, INT8, and FP16 support. Args: model (torch.nn.Mod | STRING |
| HIGH | export.py | 584 | Export a YOLOv5 model to TensorRT engine format, requiring GPU and TensorRT>=7.0.0. Args: model (torch.nn.M | STRING |
| HIGH | export.py | 703 | Export a YOLOv5 model to the TensorFlow SavedModel format, supporting dynamic axes and non-maximum suppression (NMS) | STRING |
| HIGH | export.py | 786 | Export YOLOv5 model to TensorFlow GraphDef (*.pb) format. Args: keras_model (tf.keras.Model): The Keras mod | STRING |
| HIGH | export.py | 826 | Export a YOLOv5 model to TensorFlow Lite format with optional INT8 quantization and NMS support. Args: kera | STRING |
| HIGH | export.py | 899 | Exports a YOLOv5 model to Edge TPU compatible TFLite format; requires Linux and Edge TPU compiler. Args: fi | STRING |
| HIGH | export.py | 981 | Convert a YOLOv5 model to TensorFlow.js format with optional uint8 quantization. Args: file (Path): Path to | STRING |
| HIGH | export.py | 1042 | Adds metadata to a TensorFlow Lite (TFLite) model file, supporting multiple outputs according to TensorFlow guidelin | STRING |
| HIGH | export.py | 1103 | Convert a PyTorch YOLOv5 model to CoreML format with Non-Maximum Suppression (NMS), handling different input/output | STRING |
| HIGH | export.py | 1302 | Exports a YOLOv5 model to specified formats including ONNX, TensorRT, CoreML, and TensorFlow. Args: data (s | STRING |
| HIGH | export.py | 1482 | Parse command-line options for YOLOv5 model export configurations. Args: known (bool): If True, uses `argpa | STRING |
| HIGH | train.py | 107 | Train a YOLOv5 model on a custom dataset using specified hyperparameters, options, and device, managing datasets, mo | STRING |
| HIGH | train.py | 548 | Parse command-line arguments for YOLOv5 training, validation, and testing. Args: known (bool, optional): If | STRING |
| HIGH | train.py | 622 | Runs the main entry point for training or hyperparameter evolution with specified options and optional callbacks. A | STRING |
| HIGH | train.py | 894 | Generate an individual with random hyperparameters within specified ranges. Args: input_ranges (list[tuple[ | STRING |
| HIGH | train.py | 924 | Execute YOLOv5 training with specified options, allowing optional overrides through keyword arguments. Args: | STRING |
| HIGH | hubconf.py | 17 | Creates or loads a YOLOv5 model, with options for pretrained weights and model customization. Args: name (s | STRING |
| HIGH | hubconf.py | 107 | Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification. Args: | STRING |
| HIGH | hubconf.py | 138 | Instantiates the YOLOv5-nano model with options for pretraining, input channels, class count, autoshaping, verbosity | STRING |
| HIGH | hubconf.py | 174 | Create a YOLOv5-small (yolov5s) model with options for pretraining, input channels, class count, autoshaping, verbos | STRING |
| HIGH | hubconf.py | 215 | Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping, verbosi | STRING |
| HIGH | hubconf.py | 248 | Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device selec | STRING |
| HIGH | hubconf.py | 278 | Create the YOLOv5-xlarge model with options for pretraining, input channels, class count, autoshaping, verbosity, an | STRING |
| HIGH | hubconf.py | 308 | Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and device. A | STRING |
| HIGH | hubconf.py | 335 | Instantiate the YOLOv5-small-P6 model with options for pretraining, input channels, number of classes, autoshaping, | STRING |
| HIGH | hubconf.py | 372 | Create YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity, and | STRING |
| HIGH | hubconf.py | 406 | Instantiate the YOLOv5-large-P6 model with options for pretraining, channel and class counts, autoshaping, verbosity | STRING |
| HIGH | hubconf.py | 438 | Creates the YOLOv5-xlarge-P6 model with options for pretraining, number of input channels, class count, autoshaping, | STRING |
| HIGH | detect.py | 102 | Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc. Args: we | STRING |
| HIGH | detect.py | 331 | Parse command-line arguments for YOLOv5 detection, allowing custom inference options and model configurations. Args | STRING |
| HIGH | detect.py | 417 | Executes YOLOv5 model inference based on provided command-line arguments, validating dependencies before running. A | STRING |
| HIGH | benchmarks.py | 63 | Run YOLOv5 benchmarks on multiple export formats and log results for model performance evaluation. Args: we | STRING |
| HIGH | benchmarks.py | 159 | Run YOLOv5 export tests for all supported formats and log the results, including export statuses. Args: wei | STRING |
| HIGH | benchmarks.py | 217 | Parses command-line arguments for YOLOv5 model inference configuration. Args: weights (str): The path to th | STRING |
| HIGH | benchmarks.py | 255 | Executes YOLOv5 benchmark tests or main training/inference routines based on the provided command-line arguments. A | STRING |
| HIGH | utils/metrics.py | 337 | Returns the intersection over box2 area given box1, box2. Args: box1: np.array of shape(4) box2: np | STRING |
| HIGH | utils/autoanchor.py | 69 | Creates kmeans-evolved anchors from training dataset. Args: dataset: path to data.yaml, or a loaded dataset | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 381 | except Exception as e: | CODE |
| LOW | export.py | 217 | except Exception as e: | CODE |
| LOW | export.py | 373 | except Exception as e: | CODE |
| LOW | export.py | 620 | except Exception: | CODE |
| LOW | export.py | 739 | except Exception: | CODE |
| LOW | hubconf.py | 84 | except Exception: | CODE |
| LOW | hubconf.py | 100 | except Exception as e: | CODE |
| LOW | benchmarks.py | 127 | except Exception as e: | CODE |
| LOW | benchmarks.py | 204 | except Exception: | CODE |
| LOW | segment/val.py | 389 | except Exception as e: | CODE |
| LOW | tests/test_invariant_export.py | 103 | except Exception: | CODE |
| LOW | utils/autobatch.py | 53 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 583 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 593 | except Exception: | CODE |
| LOW | utils/dataloaders.py | 749 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 1028 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 1059 | except Exception as e: | CODE |
| LOW | utils/dataloaders.py | 1101 | except Exception as e: # use OpenCV | CODE |
| LOW | utils/downloads.py | 66 | except Exception as e: # url2 | CODE |
| LOW⚡ | utils/downloads.py | 108 | except Exception: | CODE |
| LOW⚡ | utils/downloads.py | 111 | except Exception: | CODE |
| LOW⚡ | utils/downloads.py | 114 | except Exception: | CODE |
| LOW | utils/plots.py | 380 | except Exception as e: | CODE |
| LOW | utils/autoanchor.py | 142 | except Exception: | CODE |
| LOW | utils/torch_utils.py | 170 | except Exception: | CODE |
| LOW | utils/torch_utils.py | 181 | except Exception: # no backward method | CODE |
| LOW | utils/torch_utils.py | 191 | except Exception as e: | CODE |
| LOW | utils/torch_utils.py | 298 | except Exception: | CODE |
| LOW | utils/general.py | 286 | except Exception: | CODE |
| LOW | utils/general.py | 373 | except Exception as e: | CODE |
| LOW | utils/general.py | 517 | except Exception: | CODE |
| LOW | utils/general.py | 1035 | except Exception: | CODE |
| LOW | utils/augmentations.py | 51 | except Exception as e: | CODE |
| LOW | utils/augmentations.py | 293 | except Exception as e: | CODE |
| LOW | utils/loggers/__init__.py | 466 | except Exception as e: | CODE |
| LOW | utils/loggers/comet/comet_utils.py | 64 | except Exception as e: | CODE |
| LOW | utils/segment/plots.py | 146 | except Exception as e: | CODE |
| LOW | utils/flask_rest_api/restapi.py | 55 | except Exception: | CODE |
| LOW | models/yolo.py | 494 | except Exception as e: | CODE |
| MEDIUM | models/yolo.py | 495 | 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 | 137 | except Exception as e: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 113 | CODE | |
| LOW | val.py | 473 | CODE | |
| LOW | train.py | 106 | CODE | |
| LOW | train.py | 621 | CODE | |
| LOW | hubconf.py | 16 | CODE | |
| LOW | detect.py | 71 | CODE | |
| LOW | classify/val.py | 53 | CODE | |
| LOW | classify/predict.py | 68 | CODE | |
| LOW | classify/train.py | 79 | CODE | |
| LOW | segment/val.py | 105 | CODE | |
| LOW | segment/val.py | 436 | CODE | |
| LOW | segment/predict.py | 70 | CODE | |
| LOW | segment/train.py | 101 | CODE | |
| LOW | segment/train.py | 593 | CODE | |
| LOW | utils/metrics.py | 224 | CODE | |
| LOW | utils/dataloaders.py | 983 | CODE | |
| LOW | utils/dataloaders.py | 320 | CODE | |
| LOW | utils/dataloaders.py | 535 | CODE | |
| LOW | utils/dataloaders.py | 859 | CODE | |
| LOW | utils/downloads.py | 80 | CODE | |
| LOW | utils/plots.py | 135 | CODE | |
| LOW | utils/torch_utils.py | 72 | CODE | |
| LOW | utils/torch_utils.py | 144 | CODE | |
| LOW | utils/torch_utils.py | 208 | CODE | |
| LOW | utils/torch_utils.py | 305 | CODE | |
| LOW | utils/general.py | 395 | CODE | |
| LOW | utils/general.py | 435 | CODE | |
| LOW | utils/general.py | 560 | CODE | |
| LOW | utils/general.py | 563 | CODE | |
| LOW | utils/augmentations.py | 248 | CODE | |
| LOW | utils/segment/dataloaders.py | 235 | CODE | |
| LOW | utils/segment/loss.py | 48 | CODE | |
| LOW | utils/segment/plots.py | 20 | CODE | |
| LOW | utils/segment/plots.py | 116 | CODE | |
| LOW | utils/segment/general.py | 132 | CODE | |
| LOW | models/tf.py | 504 | CODE | |
| LOW | models/common.py | 458 | CODE | |
| LOW | models/common.py | 692 | CODE | |
| LOW | models/common.py | 887 | CODE | |
| LOW | models/common.py | 978 | CODE | |
| LOW | models/yolo.py | 380 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 334 | # Print results | COMMENT |
| LOW | val.py | 340 | # Print results per class | COMMENT |
| LOW | detect.py | 253 | # Print results | COMMENT |
| LOW | detect.py | 318 | # Print results | COMMENT |
| LOW | benchmarks.py | 135 | # Print results | COMMENT |
| LOW | benchmarks.py | 207 | # Print results | COMMENT |
| LOW | classify/val.py | 141 | # Print results | COMMENT |
| LOW | classify/predict.py | 153 | # Print results | COMMENT |
| LOW | classify/predict.py | 197 | # Print results | COMMENT |
| LOW | segment/val.py | 341 | # Print results | COMMENT |
| LOW | segment/val.py | 347 | # Print results per class | COMMENT |
| LOW | segment/predict.py | 183 | # Print results | COMMENT |
| LOW | segment/predict.py | 247 | # 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 | 454 | # Check if running Experiment with Comet Optimizer | COMMENT |
| LOW | utils/loggers/clearml/clearml_utils.py | 146 | # 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 | requirements.txt | 21 | # Logging --------------------------------------------------------------------- | COMMENT |
| LOW | requirements.txt | 41 | packaging # Migration of deprecated pkg_resources packages | COMMENT |
| LOW | pyproject.toml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | COMMENT |
| LOW | utils/autoanchor.py | 141 | assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar | COMMENT |
| LOW | models/common.py | 461 | # TorchScript: *.torchscript | COMMENT |
| LOW | data/ImageNet1000.yaml | 1 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | 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/coco128-seg.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/ImageNet10.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/ImageNet100.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/ImageNet.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 |
| LOW | data/scripts/get_imagenet1000.sh | 1 | #!/bin/bash | COMMENT |
| LOW | data/scripts/get_imagenet100.sh | 1 | #!/bin/bash | COMMENT |
| LOW | data/scripts/get_imagenet10.sh | 1 | #!/bin/bash | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/test_invariant_export.py | 21 | def test_shell_injection_via_model_path(malicious_path): | CODE |
| LOW | tests/test_invariant_export.py | 39 | def test_export_edgetpu_no_shell_true(): | CODE |
| LOW⚡ | tests/test_flask_rest_api.py | 57 | def test_rejects_non_image_upload_with_allowed_extension(client): | CODE |
| LOW⚡ | tests/test_flask_rest_api.py | 63 | def test_rejects_upload_with_disallowed_extension(client): | CODE |
| LOW⚡ | tests/test_flask_rest_api.py | 69 | def test_rejects_oversized_upload(client): | CODE |
| LOW⚡ | tests/test_flask_rest_api.py | 75 | def test_accepts_valid_image_upload(client): | CODE |
| LOW | tests/test_invariant_common.py | 42 | def test_ssrf_redirect_target_is_validated(monkeypatch): | CODE |
| LOW | tests/test_invariant_common.py | 72 | def test_scale_image_many_masks(): | CODE |
| LOW | tests/test_invariant_common.py | 83 | def test_tensorrt_deserialize_failure_reports_environment(monkeypatch, tmp_path): | CODE |
| LOW | utils/triton.py | 36 | def create_input_placeholders() -> list[InferInput]: | CODE |
| LOW | utils/dataloaders.py | 1201 | def create_classification_dataloader( | CODE |
| LOW | utils/torch_utils.py | 72 | def reshape_classifier_output(model, n=1000): | CODE |
| LOW | utils/torch_utils.py | 96 | def torch_distributed_zero_first(local_rank: int): | CODE |
| LOW | utils/loggers/__init__.py | 175 | def on_pretrain_routine_start(self): | CODE |
| LOW | utils/loggers/comet/__init__.py | 369 | def download_dataset_artifact(self, artifact_path): | CODE |
| LOW | utils/loggers/comet/comet_utils.py | 21 | def download_model_checkpoint(opt, experiment): | CODE |
| LOW | utils/segment/metrics.py | 17 | def ap_per_class_box_and_mask( | CODE |
| LOW | models/tf.py | 707 | def representative_dataset_gen(dataset, ncalib=100): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | val.py | 113 | CODE | |
| LOW | export.py | 689 | CODE | |
| LOW | export.py | 1275 | CODE | |
| LOW | detect.py | 71 | CODE | |
| LOW | benchmarks.py | 52 | CODE | |
| LOW | benchmarks.py | 148 | CODE | |
| LOW | classify/val.py | 53 | CODE | |
| LOW | classify/predict.py | 68 | CODE | |
| LOW | segment/val.py | 105 | CODE | |
| LOW | segment/predict.py | 70 | CODE | |
| LOW | utils/dataloaders.py | 158 | CODE | |
| LOW | utils/dataloaders.py | 535 | CODE | |
| LOW | utils/general.py | 785 | CODE | |
| LOW | utils/augmentations.py | 118 | CODE | |
| LOW | utils/augmentations.py | 248 | CODE | |
| LOW | utils/segment/dataloaders.py | 21 | CODE | |
| LOW | utils/segment/dataloaders.py | 89 | CODE | |
| LOW | utils/segment/augmentations.py | 26 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| CRITICAL | export.py | 1253 | pipeline.spec.description.metadata.userDefined.update( | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils/triton.py | 4 | CODE | |
| LOW | utils/__init__.py | 6 | CODE | |
| LOW | utils/torch_utils.py | 16 | CODE | |
| LOW | utils/general.py | 4 | CODE | |
| LOW | utils/general.py | 45 | CODE | |
| LOW | utils/general.py | 46 | CODE | |
| LOW | utils/general.py | 49 | CODE | |
| LOW | utils/general.py | 49 | CODE | |
| LOW | utils/general.py | 52 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | train.py | 853 | # Create the next generation through crossover and mutation | COMMENT |
| MEDIUM | detect.py | 190 | # Define the path for the CSV file | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | pyproject.toml | 14 | # For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com/yolov5/ | COMMENT |
| MEDIUM | models/common.py | 851 | """AutoShape class for robust YOLOv5 inference with preprocessing, NMS, and support for various input formats.""" | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils/loggers/comet/__init__.py | 10 | logger = logging.getLogger(__name__) | CODE |
| LOW | utils/loggers/comet/comet_utils.py | 14 | logger = logging.getLogger(__name__) | CODE |
| LOW | utils/loggers/comet/hpo.py | 12 | logger = logging.getLogger(__name__) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils/autobatch.py | 21 | # Usage: | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | utils/augmentations.py | 275 | # TODO: implement AugMix, AutoAug & RandAug in albumentation | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | export.py | 41 | $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example | STRING |