Repository Analysis

ultralytics/ultralytics

Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

17.2 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of ultralytics/ultralytics, a Python project with 59,458 GitHub stars. SynthScan v2.0 examined 190,407 lines of code across 958 source files, recording 1580 pattern matches distributed across 19 syntactic categories. The overall adjusted score of 17.2 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).

17.2
Adjusted Score
17.2
Raw Score
100%
Time Factor
2026-07-14
Last Push
59.5K
Stars
Python
Language
190.4K
Lines of Code
958
Files
1.6K
Pattern Hits
2026-07-14
Scan Date
0.42
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 1HIGH 404MEDIUM 44LOW 1131

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 1580 distinct pattern matches across 19 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.

Docstring Block Structure338 hits · 1665 pts
SeverityFileLineSnippetContext
HIGHCONTRIBUTING.md64Example function demonstrating Google-style docstrings. Args: arg1 (int): The first argument. arg2 STRING
HIGHCONTRIBUTING.md87Example function demonstrating Google-style docstrings. Args: arg1: The first argument. arg2: The sSTRING
HIGHdocs/build_docs.py348Remove comments and empty lines from a string of code, preserving newlines and URLs. Args: content (str): CSTRING
HIGHdocs/en/help/contributing.md62Example function demonstrating Google-style docstrings. Args: arg1 (int): The first argumenSTRING
HIGHdocs/en/help/contributing.md84Example function demonstrating Google-style docstrings. Args: arg1 (int): The first argumenSTRING
HIGHdocs/en/help/contributing.md106Example function demonstrating Google-style docstrings. Args: arg1 (int): The first argumenSTRING
HIGHdocs/en/help/contributing.md147Example function demonstrating Google-style docstrings. Args: arg1: The first argument. STRING
HIGHultralytics/nn/tasks.py1484Context manager for temporarily adding or modifying modules in Python's module cache (`sys.modules`). This functionSTRING
HIGHultralytics/nn/tasks.py1669Attempt to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised, it catches the eSTRING
HIGHultralytics/nn/text_model.py89Convert input texts to CLIP tokens. Args: texts (str | list[str]): Input text or list of texts to tSTRING
HIGHultralytics/nn/text_model.py110Encode tokenized texts into normalized feature vectors. This method processes tokenized text inputs through theSTRING
HIGHultralytics/nn/text_model.py135Encode images into normalized feature vectors. This method processes image inputs through the CLIP model to genSTRING
HIGHultralytics/nn/text_model.py220Convert input texts to MobileCLIP tokens. Args: texts (list[str]): List of text strings to tokenizeSTRING
HIGHultralytics/nn/text_model.py236Encode tokenized texts into normalized feature vectors. Args: texts (torch.Tensor): Tokenized text STRING
HIGHultralytics/nn/text_model.py297Convert input texts to MobileCLIP tokens. Args: texts (list[str]): List of text strings to tokenizeSTRING
HIGHultralytics/nn/text_model.py318Encode tokenized texts into normalized feature vectors. Args: texts (torch.Tensor): Tokenized text STRING
HIGHultralytics/nn/text_model.py339Build a text encoding model based on the specified variant. Args: variant (str): Model variant in format "bSTRING
HIGHultralytics/nn/autobackend.py38Check class names and convert to dict format if needed. Args: names (list | dict): Class names as list or dSTRING
HIGHultralytics/nn/autobackend.py230Delegate attribute access to the backend. This allows AutoBackend to transparently expose backend attributes STRING
HIGHultralytics/nn/autobackend.py321Take a path to a model file and return the model format string. Args: p (str): Path to the model fiSTRING
HIGHultralytics/nn/modules/utils.py16Create a list of cloned modules from the given module. Args: module (nn.Module): The module to be cloned. STRING
HIGHultralytics/nn/modules/utils.py36Initialize conv/fc bias value according to a given probability value. This function calculates the bias initializatSTRING
HIGHultralytics/nn/modules/utils.py78Calculate the inverse sigmoid function for a tensor. This function applies the inverse of the sigmoid function to aSTRING
HIGHultralytics/nn/modules/block.py1437C2PSA module with attention mechanism for enhanced feature extraction and processing. This module implements a convSTRING
HIGHultralytics/solutions/object_blurrer.py50Apply a blurring effect to detected objects in the input image. This method extracts tracking information, applSTRING
HIGHultralytics/solutions/security_alarm.py116Monitor the frame, process object detections, and trigger alerts if thresholds are met. This method processes tSTRING
HIGHultralytics/solutions/distance_calculation.py71Process a video frame and calculate the distance between two selected bounding boxes. This method extracts tracSTRING
HIGHultralytics/solutions/trackzone.py50Process the input frame to track objects within a defined region. This method initializes the annotator, createSTRING
HIGHultralytics/solutions/object_counter.py141Process input data (frames or object tracks) and update object counts. This method initializes the counting regSTRING
HIGHultralytics/solutions/instance_segmentation.py52Perform instance segmentation on the input image and annotate the results. Args: im0 (np.ndarray): STRING
HIGHultralytics/solutions/similarity_search.py137Return top-k semantically similar images to the given query. Args: query (str): Natural language teSTRING
HIGHultralytics/solutions/queue_management.py48Process queue management for a single frame of video. Args: im0 (np.ndarray): Input image for proceSTRING
HIGHultralytics/solutions/parking_management.py222Process the input image for parking lot management and visualization. This function analyzes the input image, eSTRING
HIGHultralytics/solutions/ai_gym.py50Monitor workouts using Ultralytics YOLO Pose Model. This function processes an input image to track and analyzeSTRING
HIGHultralytics/solutions/vision_eye.py39Perform object detection, vision mapping, and annotation on the input image. Args: im0 (np.ndarray)STRING
HIGHultralytics/solutions/object_cropper.py51Crop detected objects from the input image and save them as separate images. Args: im0 (np.ndarray)STRING
HIGHultralytics/solutions/solutions.py206Return the axis-aligned box [x1, y1, x2, y2] enclosing a box extracted by `extract_tracks`. Boxes from OBB modeSTRING
HIGHultralytics/solutions/solutions.py234Display the results of the processing, which could involve showing frames, printing counts, or saving results. STRING
HIGHultralytics/solutions/solutions.py492Draw specific keypoints for gym steps counting. Args: keypoints (list[list[float]]): Keypoints dataSTRING
HIGHultralytics/solutions/analytics.py96Process image data and run object tracking to update analytics charts. Args: im0 (np.ndarray): InpuSTRING
HIGHultralytics/solutions/analytics.py141Update the graph with new data for single or multiple classes. Args: frame_number (int): The currenSTRING
HIGHultralytics/solutions/speed_estimation.py62Process an input frame to estimate object speeds based on tracking data. Args: im0 (np.ndarray): InSTRING
HIGHultralytics/optim/muon.py10Compute the zeroth power / orthogonalization of matrix G using Newton-Schulz iteration. This function implements a STRING
HIGHultralytics/optim/muon.py60Compute Muon optimizer update with momentum and orthogonalization. This function applies momentum to the gradient, STRING
HIGHultralytics/optim/muon.py100Hybrid optimizer combining Muon and SGD updates for neural network training. This optimizer implements a combinatioSTRING
HIGHultralytics/optim/muon.py181Perform a single optimization step. Applies either hybrid Muon+SGD updates or pure SGD updates depending on theSTRING
HIGHultralytics/optim/muon.py255Muon optimizer for usage in non-distributed settings. This optimizer implements the Muon algorithm, which combines STRING
HIGHultralytics/optim/muon.py298Perform a single optimization step. Applies Muon updates to all parameters, incorporating momentum and orthogonSTRING
HIGHultralytics/utils/instance.py36A class for handling bounding boxes in multiple formats. The class supports various bounding box formats like 'xyxySTRING
HIGHultralytics/utils/instance.py141Concatenate a list of Bboxes objects into a single Bboxes object. Args: boxes_list (list[Bboxes]): STRING
HIGHultralytics/utils/instance.py163Retrieve a specific bounding box or a set of bounding boxes using indexing. Args: index (int | slicSTRING
HIGHultralytics/utils/instance.py319Retrieve a specific instance or a set of instances using indexing. Args: index (int | slice | np.ndSTRING
HIGHultralytics/utils/instance.py438Concatenate a list of Instances objects into a single Instances object. Args: instances_list (list[STRING
HIGHultralytics/utils/metrics.py239Calculate probabilistic IoU between oriented bounding boxes. Args: obb1 (torch.Tensor): Ground truth OBBs, STRING
HIGHultralytics/utils/metrics.py621Generate a summarized representation of the confusion matrix as a list of dictionaries, with optional normalizatSTRING
HIGHultralytics/utils/metrics.py1235Generate a summarized representation of per-class detection metrics as a list of dictionaries. Includes shared sSTRING
HIGHultralytics/utils/metrics.py1392Generate a summarized representation of per-class segmentation metrics as a list of dictionaries. Includes both STRING
HIGHultralytics/utils/metrics.py1543Generate a summarized representation of per-class pose metrics as a list of dictionaries. Includes both box and STRING
HIGHultralytics/utils/metrics.py1632Generate a single-row summary of classification metrics (Top-1 and Top-5 accuracy). Args: normalizeSTRING
HIGHultralytics/utils/files.py58Context manager to handle paths with spaces in their names. If a path contains spaces, it replaces them with undersSTRING
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Unused Imports420 hits · 387 pts
SeverityFileLineSnippetContext
LOWtests/conftest.py6CODE
LOWdocs/build_docs.py23CODE
LOWdocs/build_reference.py11CODE
LOWexamples/YOLO-Axelera-Python/yolo11-seg.py19CODE
LOWexamples/YOLO-Axelera-Python/yolo26-pose-tracker.py30CODE
LOWexamples/YOLOv8-Region-Counter/yolov8_region_counter.py3CODE
LOWexamples/YOLOv8-OpenCV-ONNX-Python/main.py3CODE
LOWexamples/YOLOv8-ONNXRuntime/main.py3CODE
LOW…es/YOLO-Interactive-Tracking-UI/interactive_tracker.py3CODE
LOW…amples/YOLOv8-Action-Recognition/action_recognition.py3CODE
LOWexamples/RTDETR-ONNXRuntime-Python/main.py3CODE
LOWexamples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py3CODE
LOW.github/scripts/create-export-env.py4CODE
LOWultralytics/__init__.py13CODE
LOWultralytics/__init__.py14CODE
LOWultralytics/__init__.py15CODE
LOWultralytics/__init__.py32CODE
LOWultralytics/__init__.py32CODE
LOWultralytics/__init__.py32CODE
LOWultralytics/__init__.py32CODE
LOWultralytics/__init__.py32CODE
LOWultralytics/__init__.py32CODE
LOWultralytics/__init__.py32CODE
LOWultralytics/nn/text_model.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/__init__.py3CODE
LOWultralytics/nn/distill_model.py3CODE
LOWultralytics/nn/autobackend.py3CODE
LOWultralytics/nn/autobackend.py308CODE
LOWultralytics/nn/backends/deepx.py3CODE
LOWultralytics/nn/backends/qnn.py3CODE
LOWultralytics/nn/backends/triton.py3CODE
LOWultralytics/nn/backends/paddle.py3CODE
LOWultralytics/nn/backends/onnx.py3CODE
LOWultralytics/nn/backends/onnx.py166CODE
LOWultralytics/nn/backends/openvino.py3CODE
LOWultralytics/nn/backends/__init__.py9CODE
LOWultralytics/nn/backends/__init__.py10CODE
LOWultralytics/nn/backends/__init__.py11CODE
LOWultralytics/nn/backends/__init__.py12CODE
LOWultralytics/nn/backends/__init__.py13CODE
LOWultralytics/nn/backends/__init__.py14CODE
LOWultralytics/nn/backends/__init__.py15CODE
LOWultralytics/nn/backends/__init__.py16CODE
LOWultralytics/nn/backends/__init__.py17CODE
LOWultralytics/nn/backends/__init__.py17CODE
LOWultralytics/nn/backends/__init__.py18CODE
LOWultralytics/nn/backends/__init__.py19CODE
LOWultralytics/nn/backends/__init__.py20CODE
LOWultralytics/nn/backends/__init__.py20CODE
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Magic Placeholder Names66 hits · 360 pts
SeverityFileLineSnippetContext
HIGHdocs/en/platform/quickstart.md301 headers = {"Authorization": "Bearer YOUR_API_KEY"}CODE
HIGHdocs/en/platform/quickstart.md313 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/quickstart.md374export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/index.md142 export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/index.md155 os.environ["ULTRALYTICS_API_KEY"] = "YOUR_API_KEY"CODE
HIGHdocs/en/platform/index.md198 headers = {"Authorization": "Bearer YOUR_API_KEY"}CODE
HIGHdocs/en/platform/index.md210 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/index.md222 headers: { Authorization: "Bearer YOUR_API_KEY" },CODE
HIGHdocs/en/platform/index.md323 export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/index.md336 os.environ["ULTRALYTICS_API_KEY"] = "YOUR_API_KEY"CODE
HIGHdocs/en/platform/index.md351 export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/deploy/inference.md147Authorization: Bearer YOUR_API_KEYCODE
HIGHdocs/en/platform/deploy/inference.md168 headers = {"Authorization": "Bearer YOUR_API_KEY"}CODE
HIGHdocs/en/platform/deploy/inference.md181 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/deploy/inference.md201 headers: { Authorization: "Bearer YOUR_API_KEY" },CODE
HIGHdocs/en/platform/deploy/inference.md439 headers = {"Authorization": "Bearer YOUR_API_KEY"}CODE
HIGHdocs/en/platform/deploy/endpoints.md286Authorization: Bearer YOUR_API_KEYCODE
HIGHdocs/en/platform/deploy/endpoints.md306 headers = {"Authorization": "Bearer YOUR_API_KEY"}CODE
HIGHdocs/en/platform/deploy/endpoints.md333 headers: { Authorization: "Bearer YOUR_API_KEY" },CODE
HIGHdocs/en/platform/deploy/endpoints.md347 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/deploy/monitoring.md173 headers = {"Authorization": "Bearer YOUR_API_KEY"}CODE
HIGHdocs/en/platform/deploy/monitoring.md200 headers: { Authorization: "Bearer YOUR_API_KEY" },CODE
HIGHdocs/en/platform/deploy/monitoring.md214 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/train/cloud-training.md214export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/train/index.md122 export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/api/index.md17 curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md73Authorization: Bearer YOUR_API_KEYCODE
HIGHdocs/en/platform/api/index.md85 curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md94 headers = {"Authorization": "Bearer YOUR_API_KEY"}CODE
HIGHdocs/en/platform/api/index.md106 headers: { Authorization: "Bearer YOUR_API_KEY" },CODE
HIGHdocs/en/platform/api/index.md211 curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md751 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md937 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md1031 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md1315 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md1907 -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md2218 yolo settings api_key=YOUR_API_KEYCODE
HIGHdocs/en/platform/api/index.md2224 export ULTRALYTICS_API_KEY=YOUR_API_KEYCODE
HIGHdocs/en/platform/api/index.md2232 settings.api_key = "YOUR_API_KEY"CODE
HIGHdocs/en/platform/api/index.md2355curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/api/index.md2362curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/api-keys.md70 export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/account/api-keys.md76 $env:ULTRALYTICS_API_KEY = "YOUR_API_KEY"CODE
HIGHdocs/en/platform/account/api-keys.md84yolo settings api_key="YOUR_API_KEY"CODE
HIGHdocs/en/platform/account/api-keys.md98api_key = "YOUR_API_KEY"CODE
HIGHdocs/en/platform/account/api-keys.md106curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/api-keys.md125export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/account/trash.md155 curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/trash.md162 curl -X POST -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/activity.md146 The routes shown below require an active platform browser session. The `Authorization: Bearer YOUR_API_KEY` header iCODE
HIGHdocs/en/platform/account/activity.md151 curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/activity.md158 curl -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/activity.md165 curl -X POST -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/activity.md175 curl -X POST -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/account/activity.md181 curl -X POST -H "Authorization: Bearer YOUR_API_KEY" \CODE
HIGHdocs/en/platform/data/index.md172 export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/platform/data/index.md181 os.environ["ULTRALYTICS_API_KEY"] = "YOUR_API_KEY"CODE
HIGHdocs/en/platform/data/datasets.md614 export ULTRALYTICS_API_KEY="YOUR_API_KEY"CODE
HIGHdocs/en/datasets/explorer/index.md102yolo settings openai_api_key="YOUR_API_KEY"CODE
HIGHdocs/en/integrations/weights-biases.md69 wandb.login(key="YOUR_API_KEY")CODE
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Excessive Try-Catch Wrapping153 hits · 159 pts
SeverityFileLineSnippetContext
LOWtests/test_exports.py238 except Exception as error: # pragma: no cover - assertion handled belowCODE
LOWdocs/build_docs.py148 except Exception as e:CODE
LOWdocs/build_docs.py187 except Exception as e:CODE
LOWdocs/build_docs.py204 except Exception:CODE
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LOWdocs/build_docs.py480 except Exception as e:CODE
LOWdocs/build_docs.py527 except Exception as e:CODE
LOWdocs/build_docs.py542 except Exception as e:CODE
LOWdocs/build_docs.py556 except Exception as e:CODE
LOWdocs/build_docs.py686 except Exception as e:CODE
LOWdocs/build_reference.py193 except Exception:CODE
LOWdocs/build_reference.py646 except Exception:CODE
LOWdocs/en/datasets/detect/roboflow-100.md83 except Exception:CODE
MEDIUMdocs/en/usage/simple-utilities.md490 print("Error: Could not open video.")CODE
LOWdocs/en/guides/vertex-ai-deployment-with-docker.md123 except Exception as e:CODE
MEDIUMdocs/en/guides/vertex-ai-deployment-with-docker.md124 print(f"Error initializing YOLO model: {e}")CODE
LOWdocs/en/guides/vertex-ai-deployment-with-docker.md205 except Exception as e:CODE
MEDIUMdocs/en/guides/vertex-ai-deployment-with-docker.md207 print(f"Error in YOLO detection: {e}")CODE
LOWdocs/en/guides/vertex-ai-deployment-with-docker.md353 except Exception as e:CODE
LOW.github/workflows/merge-main-into-prs.yml73 except Exception as update_error:CODE
LOW.github/workflows/merge-main-into-prs.yml79 except Exception as e:CODE
LOWultralytics/nn/tasks.py1589 except Exception as e:CODE
LOWultralytics/nn/tasks.py1622 except Exception: # optional/oddball submodule — skipCODE
LOWultralytics/nn/autobackend.py83 except Exception:CODE
LOWultralytics/nn/backends/tensorrt.py66 except Exception as e:CODE
LOWultralytics/nn/backends/coreml.py42 except Exception:CODE
LOWultralytics/solutions/security_alarm.py112 except Exception as e:CODE
LOWultralytics/solutions/similarity_search.py122 except Exception as e:CODE
LOWultralytics/utils/autobatch.py126 except Exception as e:CODE
LOWultralytics/utils/events.py22 except Exception:CODE
LOWultralytics/utils/checks.py131 except Exception as e:CODE
LOWultralytics/utils/checks.py327 except Exception:CODE
LOWultralytics/utils/checks.py348 except Exception:CODE
LOWultralytics/utils/checks.py417 except Exception:CODE
LOWultralytics/utils/checks.py541 except Exception as e:CODE
LOWultralytics/utils/checks.py778 except Exception as e:CODE
LOWultralytics/utils/checks.py972 except Exception:CODE
LOWultralytics/utils/checks.py1071 except Exception: # broad clause to capture all Intel GPU exception typesCODE
LOWultralytics/utils/plotting.py238 except Exception:CODE
LOWultralytics/utils/plotting.py892 except Exception:CODE
LOWultralytics/utils/plotting.py959 except Exception as e:CODE
LOWultralytics/utils/cpu.py57 except Exception:CODE
LOWultralytics/utils/cpu.py63 except Exception:CODE
LOWultralytics/utils/__init__.py658 except Exception as e:CODE
LOWultralytics/utils/__init__.py663 except Exception:CODE
LOWultralytics/utils/__init__.py803 except Exception:CODE
LOWultralytics/utils/__init__.py832 except Exception:CODE
LOWultralytics/utils/__init__.py1148 except Exception as e:CODE
LOWultralytics/utils/__init__.py1304 except Exception as e:CODE
LOWultralytics/utils/__init__.py1313 except Exception as e:CODE
LOWultralytics/utils/__init__.py237 except Exception:STRING
LOWultralytics/utils/logger.py103 except Exception:CODE
LOWultralytics/utils/logger.py124 except Exception:CODE
LOWultralytics/utils/logger.py237 except Exception:CODE
LOWultralytics/utils/logger.py256 except Exception as e:CODE
LOWultralytics/utils/logger.py494 except Exception as e:CODE
LOWultralytics/utils/logger.py635 except Exception:CODE
LOWultralytics/utils/tqdm.py287 except Exception:CODE
LOWultralytics/utils/tqdm.py333 except Exception:CODE
LOWultralytics/utils/tqdm.py360 except Exception:CODE
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Structural Annotation Overuse104 hits · 156 pts
SeverityFileLineSnippetContext
LOWdocs/en/platform/train/cloud-training.md33### Step 1: Select Base ModelCOMMENT
LOWdocs/en/platform/train/cloud-training.md44### Step 2: Select DatasetCOMMENT
LOWdocs/en/platform/train/cloud-training.md61### Step 3: Configure ParametersCOMMENT
LOWdocs/en/platform/train/cloud-training.md72### Step 4: Advanced Settings (Optional)COMMENT
LOWdocs/en/platform/train/cloud-training.md102### Step 5: Select GPU (Cloud Tab)COMMENT
LOWdocs/en/platform/train/cloud-training.md120### Step 6: Start TrainingCOMMENT
LOWdocs/en/integrations/hailo.md87### Step 1: Install UltralyticsCOMMENT
LOWdocs/en/integrations/hailo.md93### Step 2: Install Hailo DFC SDKCOMMENT
LOWdocs/en/integrations/hailo.md150 # Step 1: Export to ONNX, then move it into the deploy folder to keep the working directory tidyCOMMENT
LOWdocs/en/integrations/hailo.md166 # Step 2: Parse ONNX with Hailo DFCCOMMENT
LOWdocs/en/integrations/hailo.md171 # Step 3: Load model script (normalization + HailoRT NMS)COMMENT
LOWdocs/en/integrations/hailo.md183 # Step 4: Build calibration dataset (auto-downloads COCO128)COMMENT
LOWdocs/en/integrations/hailo.md195 # Step 5: Optimize and quantizeCOMMENT
LOWdocs/en/integrations/hailo.md199 # Step 6: Compile to HEFCOMMENT
LOWdocs/en/integrations/hailo.md223### Step 1: Export to ONNX and Save MetadataCOMMENT
LOWdocs/en/integrations/hailo.md229### Step 2: Parse the ONNX ModelCOMMENT
LOWdocs/en/integrations/hailo.md243### Step 3: Load the Model ScriptCOMMENT
LOWdocs/en/integrations/hailo.md271### Step 4: Build the Calibration DatasetCOMMENT
LOWdocs/en/integrations/hailo.md279### Step 5: Optimize and QuantizeCOMMENT
LOWdocs/en/integrations/hailo.md283### Step 6: Compile to HEFCOMMENT
LOWdocs/en/integrations/hailo.md359### Step 1: Install HailoRT on the DeviceCOMMENT
LOWdocs/en/integrations/hailo.md373### Step 2: Quick Sanity CheckCOMMENT
LOWdocs/en/integrations/hailo.md393### Step 3: Run InferenceCOMMENT
LOWdocs/en/integrations/neural-magic.md72### Step 1: InstallationCOMMENT
LOWdocs/en/integrations/neural-magic.md85### Step 2: Exporting YOLO26 to ONNX FormatCOMMENT
LOWdocs/en/integrations/neural-magic.md100### Step 3: Deploying and Running InferencesCOMMENT
LOWdocs/en/integrations/neural-magic.md122### Step 4: Benchmarking PerformanceCOMMENT
LOWdocs/en/integrations/neural-magic.md135### Step 5: Additional FeaturesCOMMENT
LOWdocs/en/integrations/jupyterlab.md54### Step 1: Install JupyterLabCOMMENT
LOWdocs/en/integrations/jupyterlab.md67### Step 2: Download the YOLO26 Tutorial NotebookCOMMENT
LOWdocs/en/integrations/jupyterlab.md71### Step 3: Launch JupyterLabCOMMENT
LOWdocs/en/integrations/jupyterlab.md87### Step 4: Start ExperimentingCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md48### Step 1: Set Up Your EnvironmentCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md56### Step 2: Install and Import Relevant LibrariesCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md87### Step 3: Load the DataCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md142### Step 4: Preprocess the DataCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md239### Step 5: Train the YOLO26 modelCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md262### Step 6: Test the ModelCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md295### Step 7: Evaluate the ModelCOMMENT
LOWdocs/en/integrations/ibm-watsonx.md313### Step 8: Calculating [Intersection Over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou)COMMENT
LOWdocs/en/integrations/clearml.md71 # Step 1: Creating a ClearML TaskCOMMENT
LOWdocs/en/integrations/clearml.md74 # Step 2: Selecting the YOLO26 ModelCOMMENT
LOWdocs/en/integrations/clearml.md78 # Step 3: Loading the YOLO26 ModelCOMMENT
LOWdocs/en/integrations/clearml.md81 # Step 4: Setting Up Training ArgumentsCOMMENT
LOWdocs/en/integrations/clearml.md85 # Step 5: Initiating Model TrainingCOMMENT
LOWdocs/en/integrations/clearml.md241# Step 1: Creating a ClearML TaskCOMMENT
LOWdocs/en/integrations/clearml.md244# Step 2: Selecting the YOLO26 ModelCOMMENT
LOWdocs/en/integrations/clearml.md248# Step 3: Loading the YOLO26 ModelCOMMENT
LOWdocs/en/integrations/clearml.md251# Step 4: Setting Up Training ArgumentsCOMMENT
LOWdocs/en/integrations/clearml.md255# Step 5: Initiating Model TrainingCOMMENT
LOWdocs/en/integrations/amazon-sagemaker.md30### Step 1: Setup Your AWS EnvironmentCOMMENT
LOWdocs/en/integrations/amazon-sagemaker.md44### Step 2: Clone the YOLO26 SageMaker RepositoryCOMMENT
LOWdocs/en/integrations/amazon-sagemaker.md60### Step 3: Set Up the CDK EnvironmentCOMMENT
LOWdocs/en/integrations/amazon-sagemaker.md88### Step 4: Create the AWS CloudFormation StackCOMMENT
LOWdocs/en/integrations/amazon-sagemaker.md108### Step 5: Deploy the YOLO ModelCOMMENT
LOWdocs/en/integrations/amazon-sagemaker.md142### Step 6: Testing Your DeploymentCOMMENT
LOWdocs/en/integrations/amazon-sagemaker.md156### Step 7: Monitoring and ManagementCOMMENT
LOW…olov5/environments/docker-image-quickstart-tutorial.md124## Step 1: Pull the YOLOv5 Docker ImageCOMMENT
LOW…olov5/environments/docker-image-quickstart-tutorial.md138## Step 2: Run the Docker ContainerCOMMENT
LOW…olov5/environments/docker-image-quickstart-tutorial.md176## Step 3: Use YOLOv5 🚀 within the Docker ContainerCOMMENT
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Hyper-Verbose Identifiers153 hits · 142 pts
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LOWtests/test_solutions.py218def test_object_counter_polygon_direction(region, step, expected):CODE
LOWtests/test_solutions.py233def test_object_counter_polygon_reversal_at_entry():CODE
LOWtests/test_solutions.py248def test_object_counter_polygon_reentry_after_inside_spawn():CODE
LOWtests/test_solutions.py261def test_left_click_selection():CODE
LOWtests/test_solutions.py269def test_left_click_selection_obb():CODE
LOWtests/test_solutions.py308def test_object_blurrer_obb_outside_frame():CODE
LOWtests/test_solutions.py339def test_analytics_graph_not_supported():CODE
LOWtests/test_solutions.py357def test_config_update_method_with_invalid_argument():CODE
LOWtests/test_solutions.py375def test_streamlit_handle_video_upload_creates_file(tmp_path):CODE
LOWtests/test_solutions.py408def test_similarity_search_app_init():CODE
LOWtests/test_solutions.py417def test_similarity_search_complete(tmp_path):CODE
LOWtests/test_solutions.py431def test_distance_calculation_process_method():CODE
LOWtests/test_solutions.py456def test_display_output_method():CODE
LOWtests/test_engine.py226def test_load_checkpoint_state_dict_rejected(ckpt, tmp_path):CODE
LOWtests/test_engine.py275def test_checkpoint_nonfinite_ema_resync():CODE
LOWtests/test_engine.py294def test_checkpoint_nonfinite_ema_and_model_sanitized():CODE
LOWtests/test_engine.py319def test_train_reuses_loaded_checkpoint_model(monkeypatch, kwargs, uses_weights):CODE
LOWtests/test_engine.py361def test_train_multi_custom_trainer_metrics_and_failure_keys(monkeypatch, tmp_path):CODE
LOWtests/test_engine.py394def test_setup_model_respects_pretrained_arg_for_pt_models(monkeypatch, pretrained, uses_weights):CODE
LOWtests/conftest.py55def pytest_collection_modifyitems(config, items):CODE
LOWtests/test_exports.py117def test_quantize_canonicalization():CODE
LOWtests/test_exports.py146def test_quantize_deprecation():CODE
LOWtests/test_exports.py156def test_benchmark_forwards_legacy_precision(monkeypatch):CODE
LOWtests/test_exports.py180def test_export_rknn_batch_expansion(monkeypatch, tmp_path):CODE
LOWtests/test_exports.py202def test_modelopt_quantize_onnx_excludes_sigmoid(monkeypatch):CODE
LOWtests/test_exports.py272def test_export_openvino_matrix(task, dynamic, quantize, batch, nms, end2end):CODE
LOWtests/test_exports.py319def test_export_onnx_semantic_dnn():CODE
LOWtests/test_exports.py339def test_export_torchscript_matrix(task, dynamic, batch, nms, end2end, tmp_path):CODE
LOWtests/test_exports.py368def test_export_coreml_matrix(task, dynamic, quantize, nms, batch, end2end):CODE
LOWtests/test_exports.py414def test_export_coreml_rtdetr():CODE
LOWtests/test_exports.py465def test_export_mnn_rejects_unsupported_nms(model, kwargs, error):CODE
LOWtests/test_exports.py570def test_export_litert_matrix(task, quantize):CODE
LOWtests/test_exports.py593def test_export_executorch_matrix(task):CODE
LOWtests/test_exports.py635def test_export_env_has_smoke(env):CODE
LOWtests/test_exports.py640def test_every_format_env_is_registered():CODE
LOWtests/test_python.py48def test_dataloader_caps_workers_to_batches():CODE
LOWtests/test_python.py63def test_dataloader_cap_preserves_distributed_drop_last(monkeypatch):CODE
LOWtests/test_python.py79def test_dataloader_empty_dataset_uses_dataloader_validation():CODE
LOWtests/test_python.py85def test_cfg_rejects_fuzzed_scalars():CODE
LOWtests/test_python.py187def test_model_load_remaps_cls_head_by_names():CODE
LOWtests/test_python.py243def test_predict_csv_multi_row(tmp_path):CODE
LOWtests/test_python.py255def test_predict_csv_single_row(tmp_path):CODE
LOWtests/test_python.py291def test_predict_classes_with_max_det(model_name):CODE
LOWtests/test_python.py342def test_predict_ndarray_channels():CODE
LOWtests/test_python.py358def test_predict_all_image_formats():CODE
LOWtests/test_python.py395def test_track_second_association_indices():CODE
LOWtests/test_python.py412def test_track_second_association_low_conf_keeps_id(tracker_type):CODE
LOWtests/test_python.py432def test_track_reid_auto_user_detections(tracker_type):CODE
LOWtests/test_python.py561def test_normalize_platform_uri():CODE
LOWtests/test_python.py619def test_predict_callback_and_setup():CODE
LOWtests/test_python.py671def test_results_plot_without_boxes():CODE
LOWtests/test_python.py732def test_safe_download_unzips_local_path_archive(tmp_path):CODE
LOWtests/test_python.py753def test_safe_download_skips_unsafe_archive_members(tmp_path):CODE
LOWtests/test_python.py766def test_safe_download_skips_unsafe_tar_members(tmp_path):CODE
LOWtests/test_python.py941def test_semantic_loss_all_ignore(nc):CODE
LOWtests/test_python.py1060def test_utils_patches_torch_save(tmp_path):CODE
LOWtests/test_python.py1135def test_classify_transforms_train(image, auto_augment, erasing, force_color_jitter):CODE
LOWtests/test_python.py1209def test_process_mask_native_chunked():CODE
LOWtests/test_python.py1330def test_yoloe_visual_prompt_verbose_false(capfd):CODE
LOWtests/test_python.py1405def test_semantic_polygon_data():CODE
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Deep Nesting133 hits · 126 pts
SeverityFileLineSnippetContext
LOWtests/test_solutions.py180CODE
LOWdocs/build_docs.py85CODE
LOWdocs/build_docs.py192CODE
LOWdocs/build_docs.py347CODE
LOWdocs/build_docs.py589CODE
LOWdocs/build_reference.py642CODE
LOWexamples/YOLO-Axelera-Python/yolo11-seg.py59CODE
LOWexamples/YOLOv8-Region-Counter/yolov8_region_counter.py42CODE
LOWexamples/YOLOv8-Region-Counter/yolov8_region_counter.py87CODE
LOW…es/YOLO-Interactive-Tracking-UI/interactive_tracker.py71CODE
LOW…es/YOLO-Interactive-Tracking-UI/interactive_tracker.py115CODE
LOW…amples/YOLOv8-Action-Recognition/action_recognition.py314CODE
LOW.github/scripts/fuzz.py210CODE
LOW.github/scripts/fuzz.py383CODE
LOW.github/scripts/fuzz.py539CODE
LOW.github/scripts/fuzz.py402CODE
LOWultralytics/nn/tasks.py1668CODE
LOWultralytics/nn/tasks.py1841CODE
LOWultralytics/nn/tasks.py2082CODE
LOWultralytics/nn/tasks.py236CODE
LOWultralytics/nn/tasks.py837CODE
LOWultralytics/nn/tasks.py1076CODE
LOWultralytics/nn/autobackend.py320CODE
LOWultralytics/nn/backends/onnx.py48CODE
LOWultralytics/nn/backends/tensorflow.py108CODE
LOWultralytics/nn/backends/tensorrt.py25CODE
LOWultralytics/nn/backends/litert.py50CODE
LOWultralytics/nn/modules/conv.py447CODE
LOWultralytics/solutions/distance_calculation.py44CODE
LOWultralytics/solutions/distance_calculation.py70CODE
LOWultralytics/solutions/object_counter.py54CODE
LOWultralytics/solutions/ai_gym.py49CODE
LOWultralytics/solutions/streamlit_inference.py126CODE
LOWultralytics/solutions/analytics.py138CODE
LOWultralytics/solutions/speed_estimation.py61CODE
LOWultralytics/optim/muon.py180CODE
LOWultralytics/utils/metrics.py105CODE
LOWultralytics/utils/metrics.py542CODE
LOWultralytics/utils/files.py57CODE
LOWultralytics/utils/checks.py148CODE
LOWultralytics/utils/checks.py217CODE
LOWultralytics/utils/checks.py439CODE
LOWultralytics/utils/checks.py625CODE
LOWultralytics/utils/checks.py667CODE
LOWultralytics/utils/checks.py1036CODE
LOWultralytics/utils/plotting.py717CODE
LOWultralytics/utils/plotting.py915CODE
LOWultralytics/utils/plotting.py208CODE
LOWultralytics/utils/plotting.py453CODE
LOWultralytics/utils/cpu.py32CODE
LOWultralytics/utils/__init__.py447CODE
LOWultralytics/utils/__init__.py943CODE
LOWultralytics/utils/logger.py131CODE
LOWultralytics/utils/logger.py311CODE
LOWultralytics/utils/downloads.py286CODE
LOWultralytics/utils/downloads.py470CODE
LOWultralytics/utils/ops.py652CODE
LOWultralytics/utils/loss.py496CODE
LOWultralytics/utils/tuner.py118CODE
LOWultralytics/utils/benchmarks.py63CODE
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Self-Referential Comments30 hits · 74 pts
SeverityFileLineSnippetContext
MEDIUMtests/test_cli.py119 # Create a FastSAM modelCOMMENT
MEDIUMexamples/YOLOv8-ONNXRuntime/main.py107 # Create the label text with class name and scoreCOMMENT
MEDIUMexamples/YOLOv8-ONNXRuntime/main.py260 # Create an argument parser to handle command-line argumentsCOMMENT
MEDIUMexamples/YOLOv8-ONNXRuntime/main.py271 # Create an instance of the YOLOv8 class with the specified argumentsCOMMENT
MEDIUMexamples/RTDETR-ONNXRuntime-Python/main.py139 # Create the label text with class name and scoreCOMMENT
MEDIUMexamples/RTDETR-ONNXRuntime-Python/main.py292 # Create the detector instance with specified parametersCOMMENT
MEDIUMultralytics/solutions/heatmap.py65 # Create a meshgrid with region of interest (ROI) for vectorized distance calculationsCOMMENT
MEDIUMultralytics/solutions/heatmap.py71 # Create a mask of points within the radiusCOMMENT
MEDIUMultralytics/solutions/security_alarm.py94 # Create the emailCOMMENT
MEDIUMultralytics/solutions/trackzone.py69 if self.mask is None: # Create a mask for the regionCODE
MEDIUMultralytics/solutions/streamlit_inference.py219 self.sidebar() # Create the sidebarCODE
MEDIUMultralytics/solutions/streamlit_inference.py272 # Create an instance of the Inference class and run inferenceCOMMENT
MEDIUMultralytics/solutions/analytics.py223 # Create the legend using labels from the barsCOMMENT
MEDIUMultralytics/utils/files.py79 # Create a temporary directory and construct the new pathCOMMENT
MEDIUMultralytics/utils/loss.py727 # Create a tensor to hold batched keypointsCOMMENT
MEDIUMultralytics/utils/patches.py124 >>> img = np.zeros((100, 100, 3), dtype=np.uint8) # Create a black imageSTRING
MEDIUMultralytics/utils/patches.py149 >>> img = np.zeros((300, 300, 3), dtype=np.uint8) # Create a black imageSTRING
MEDIUMultralytics/utils/tuner.py458 # Define the trainable function with allocated resourcesCOMMENT
MEDIUMultralytics/utils/tuner.py461 # Define the scheduler for hyperparameter searchCOMMENT
MEDIUMultralytics/utils/tuner.py480 # Create the Ray Tune hyperparameter search tunerCOMMENT
MEDIUMultralytics/models/sam/amg.py211 >>> mask[40:60, 40:60] = True # Create a squareSTRING
MEDIUMultralytics/models/sam/amg.py212 >>> mask[45:55, 45:55] = False # Create a holeSTRING
MEDIUMultralytics/models/sam/predict.py1911 # Initialize the object index set and mappingsCOMMENT
MEDIUMultralytics/models/sam/build_sam3.py239 # Create the SAM3SemanticModel modelCOMMENT
MEDIUMultralytics/models/sam/sam3/vitdet.py315 """This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`. "Exploring Plain Vision TransformerSTRING
MEDIUMultralytics/models/utils/ops.py156 # This function is for future RT-DETR Segment modelsCOMMENT
MEDIUMultralytics/models/utils/loss.py155 # This function is for future RT-DETR Segment modelsCOMMENT
MEDIUMultralytics/models/utils/loss.py176 # This function is for future RT-DETR Segment modelsCOMMENT
MEDIUMultralytics/data/converter.py404 # Create a binary mask for the current class and find contoursSTRING
MEDIUMultralytics/data/utils.py341 rect = plt.Rectangle((x, y), w, h, linewidth=2, edgecolor=color, facecolor="none") # Create a rectangleCODE
Redundant / Tautological Comments47 hits · 62 pts
SeverityFileLineSnippetContext
LOWdocs/build_docs.py242 # Check if parent is a span with class="normal"COMMENT
LOWdocs/build_docs.py668 # Print results and auto-serve on macOSCOMMENT
LOWdocs/build_reference.py436 # Check if text contains Markdown structures that need line preservationCOMMENT
LOWdocs/build_reference.py1133 # Check if the document paths are the same (ignoring structure or formatting differences)COMMENT
LOWexamples/YOLOv8-SAHI-Inference-Video/yolov8_sahi.py110 # Display results if requestedCOMMENT
LOWexamples/YOLOv8-Region-Counter/yolov8_region_counter.py177 # Check if detection inside regionCOMMENT
LOWexamples/YOLOv8-ONNXRuntime/main.py277 # Display the output image in a windowCOMMENT
LOW…es/YOLO-Interactive-Tracking-UI/interactive_tracker.py17save_video = False # Set True to save output videoCODE
LOWexamples/RTDETR-ONNXRuntime-Python/main.py25 # Check if the local path already existsCOMMENT
LOW.github/workflows/merge-main-into-prs.yml61 # Check if PR is behind the default branchCOMMENT
LOW.github/workflows/conda-check-prs.yml68 # Print results to action logCOMMENT
LOWultralytics/nn/autobackend.py191 # Check if format supports FP16COMMENT
LOWultralytics/nn/backends/onnx.py95 # Check if dynamic shapesCOMMENT
LOWultralytics/solutions/heatmap.py47 if self.region is not None: # Check if user provided the region coordinatesCODE
LOWultralytics/solutions/heatmap.py119 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/solutions/object_blurrer.py88 self.display_output(plot_im) # Display the output using the base class functionCODE
LOWultralytics/solutions/security_alarm.py148 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/solutions/distance_calculation.py122 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/solutions/trackzone.py85 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/solutions/object_counter.py184 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/solutions/similarity_search.py102 # Check if the embeddings and corresponding image paths already existCOMMENT
LOWultralytics/solutions/queue_management.py90 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/solutions/parking_management.py53 try: # Check if tkinter is installedCODE
LOWultralytics/solutions/parking_management.py273 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/solutions/ai_gym.py101 self.display_output(plot_im) # Display output image, if environment support displayCODE
LOWultralytics/solutions/streamlit_inference.py269 # Check if a model name is provided as a command-line argumentCOMMENT
LOWultralytics/solutions/analytics.py225 bar.set_label(label) # Assign label to each barCODE
LOWultralytics/solutions/speed_estimation.py111 self.display_output(plot_im) # Display output with base class functionCODE
LOWultralytics/utils/metrics.py428 if gt_cls.shape[0] == 0: # Check if labels is emptyCODE
LOWultralytics/utils/checks.py412 # Check if package is installed (look for "ii" status)COMMENT
LOWultralytics/utils/__init__.py651 # Read file contentCOMMENT
LOWultralytics/utils/__init__.py1422 if not self.file.exists() or not self: # Check if file doesn't exist or is emptyCODE
LOWultralytics/utils/downloads.py190 # Check if destination directory already exists and contains filesCOMMENT
LOWultralytics/utils/downloads.py371 # Check if download is complete (only if we have expected_size)COMMENT
LOWultralytics/utils/benchmarks.py240 # Print resultsCOMMENT
LOWultralytics/utils/callbacks/wb.py174 # Check if we actually have plots to saveCOMMENT
LOWultralytics/utils/export/imx.py294 # Check if the model has the expected number of layersCOMMENT
LOWultralytics/utils/export/engine.py409 # Write fileCOMMENT
LOWultralytics/models/sam/amg.py169 # Check if boxes has a channel dimensionCOMMENT
LOWultralytics/models/sam/amg.py179 # Check if points has a channel dimensionCOMMENT
LOWultralytics/models/yolo/detect/val.py289 # Print results per classCOMMENT
LOWultralytics/data/converter.py865 # Check if this is a classification datasetCOMMENT
LOWultralytics/data/converter.py910 # Check if dataset already exists (enables image reuse across split changes)COMMENT
LOWultralytics/data/dataset.py196 # Check if the dataset is all boxes or all segmentsCOMMENT
LOWultralytics/engine/predictor.py305 # Check if save_dir/ label file existsCOMMENT
LOWultralytics/engine/model.py123 # Check if Ultralytics HUB model from https://hub.ultralytics.comCOMMENT
LOWultralytics/engine/model.py134 # Check if Triton Server modelCOMMENT
AI Structural Patterns43 hits · 40 pts
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LOWexamples/YOLOv8-SAHI-Inference-Video/yolov8_sahi.py56CODE
LOWexamples/YOLOv8-Region-Counter/yolov8_region_counter.py87CODE
LOW…amples/YOLOv8-Action-Recognition/action_recognition.py314CODE
LOWultralytics/nn/modules/head.py1213CODE
LOWultralytics/nn/modules/head.py1380CODE
LOWultralytics/nn/modules/head.py1466CODE
LOWultralytics/nn/modules/conv.py371CODE
LOWultralytics/nn/modules/block.py1818CODE
LOWultralytics/utils/plotting.py717CODE
LOWultralytics/utils/nms.py13CODE
LOWultralytics/utils/tqdm.py80CODE
LOWultralytics/utils/downloads.py286CODE
LOWultralytics/utils/benchmarks.py63CODE
LOWultralytics/utils/export/engine.py177CODE
LOWultralytics/models/sam/build.py228CODE
LOWultralytics/models/sam/predict.py331CODE
LOWultralytics/models/sam/predict.py2495CODE
LOWultralytics/models/sam/modules/decoders.py210CODE
LOWultralytics/models/sam/modules/memory_attention.py54CODE
LOWultralytics/models/sam/modules/encoders.py47CODE
LOWultralytics/models/sam/modules/encoders.py643CODE
LOWultralytics/models/sam/modules/blocks.py454CODE
LOWultralytics/models/sam/modules/blocks.py871CODE
LOWultralytics/models/sam/modules/tiny_encoder.py678CODE
LOWultralytics/models/sam/modules/tiny_encoder.py786CODE
LOWultralytics/models/sam/modules/sam.py163CODE
LOWultralytics/models/sam/modules/sam.py1028CODE
LOWultralytics/models/sam/sam3/decoder.py73CODE
LOWultralytics/models/sam/sam3/decoder.py183CODE
LOWultralytics/models/sam/sam3/decoder.py377CODE
LOWultralytics/models/sam/sam3/text_encoder_ve.py148CODE
LOWultralytics/models/sam/sam3/text_encoder_ve.py238CODE
LOWultralytics/models/sam/sam3/vitdet.py44CODE
LOWultralytics/models/sam/sam3/vitdet.py219CODE
LOWultralytics/models/sam/sam3/vitdet.py319CODE
LOWultralytics/models/sam/sam3/sam3_image.py33CODE
LOWultralytics/models/utils/loss.py37CODE
LOWultralytics/data/annotator.py10CODE
LOWultralytics/data/augment.py2827CODE
LOWultralytics/data/augment.py1629CODE
LOWultralytics/data/augment.py2219CODE
LOWultralytics/data/base.py72CODE
LOWultralytics/engine/results.py461CODE
Modern Structural Boilerplate29 hits · 30 pts
SeverityFileLineSnippetContext
LOWdocs/build_reference.py1106def update_mkdocs_file(reference_yaml: str) -> None:CODE
LOWultralytics/nn/backends/__init__.py27__all__ = [CODE
LOWultralytics/optim/__init__.py5__all__ = ["MuSGD", "Muon"]CODE
LOWultralytics/utils/metrics.py1062 def update_image_metrics(self, tp: np.ndarray, target_cls: np.ndarray, pred_cls: np.ndarray, im_name: str) -> None:CODE
LOWultralytics/utils/metrics.py1138 def update_stats(self, stat: dict[str, Any]) -> None:CODE
LOWultralytics/utils/metrics.py1304 def update_stats(self, stat: dict[str, Any]) -> None:CODE
LOWultralytics/utils/metrics.py1455 def update_stats(self, stat: dict[str, Any]) -> None:CODE
LOWultralytics/utils/metrics.py1710 def update_stats(self, preds: torch.Tensor, targets: torch.Tensor) -> None:CODE
LOWultralytics/utils/tqdm.py296 def set_description(self, desc: str | None) -> None:CODE
LOWultralytics/utils/tqdm.py302 def set_postfix(self, **kwargs: Any) -> None:CODE
LOWultralytics/utils/export/__init__.py18__all__ = [CODE
LOWultralytics/models/sam/sam3/vitdet.py96 def _setup_rel_pos(self, rel_pos_zero_init: bool = True, input_size: tuple[int, int] | None = None) -> None:CODE
LOWultralytics/models/sam/sam3/vitdet.py120 def _setup_rope_freqs(self, input_size: tuple[int, int] | None = None) -> None:CODE
LOWultralytics/models/nas/val.py8__all__ = ["NASValidator"]CODE
LOWultralytics/models/yolo/model.py183 def set_classes(self, classes: list[str]) -> None:CODE
LOWultralytics/models/yolo/model.py290 def set_vocab(self, vocab: list[str], names: list[str]) -> None:CODE
LOWultralytics/models/yolo/model.py315 def set_classes(self, classes: list[str], embeddings: torch.Tensor | None = None) -> None:CODE
LOWultralytics/models/yolo/detect/val.py169 def update_metrics(self, preds: list[dict[str, torch.Tensor]], batch: dict[str, Any]) -> None:CODE
LOWultralytics/models/yolo/classify/val.py90 def update_metrics(self, preds: torch.Tensor, batch: dict[str, Any]) -> None:CODE
LOWultralytics/models/yolo/yoloe/__init__.py8__all__ = [CODE
LOWultralytics/models/yolo/world/__init__.py6__all__ = ["WorldTrainer", "WorldValidator"]CODE
LOWultralytics/models/yolo/world/train.py112 def set_text_embeddings(self, datasets: list[Any], batch: int | None) -> None:CODE
LOWultralytics/trackers/deep_oc_sort.py60 def update_features(self, feat: np.ndarray, score: float | None = None) -> None:CODE
LOWultralytics/trackers/bot_sort.py70 def update_features(self, feat: np.ndarray) -> None:CODE
LOWultralytics/trackers/track_tracker.py249 def update_features(self, feat: np.ndarray) -> None:CODE
LOWultralytics/data/build.py219 def set_epoch(self, epoch: int) -> None:CODE
LOWultralytics/data/dataset.py721 def update_labels(self, include_class: list[int] | None) -> None:CODE
LOWultralytics/data/base.py187 def update_labels(self, include_class: list[int] | None) -> None:CODE
LOWultralytics/data/base.py370 def set_rectangle(self) -> None:CODE
Over-Commented Block27 hits · 26 pts
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LOWmkdocs.yml921 - Privacy Policy: help/privacy.mdCOMMENT
LOWpyproject.toml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWdocs/en/models/yolo-world.md341COMMENT
LOWdocs/en/yolov5/tutorials/hyperparameter-evolution.md101COMMENT
LOWexamples/cpp/ONNXRuntime/inference.cpp1// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWexamples/cpp/LibTorch/CMakeLists.txt21COMMENT
LOWexamples/cpp/common/yolo_postprocess.hpp1// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWexamples/cpp/common/yolo_draw.hpp1// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWexamples/cpp/common/yolo_render.hpp1// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOW.github/workflows/mirror.yml41 # git remote add gitcode https://ultralytics:${{ secrets.GITCODE_TOKEN }}@gitcode.net/ultralytics/ultralytics.COMMENT
LOW.github/workflows/docs.yml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/models/sam/predict.py1501 out = (COMMENT
LOWultralytics/models/utils/ops.py161 # sample_points = 2.0 * sample_points - 1.0COMMENT
LOWultralytics/models/utils/loss.py161 # loss = {}COMMENT
LOWultralytics/cfg/datasets/dota8-multispectral.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/hand-keypoints.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/medical-pills.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/cityscapes8.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/xView.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/dog-pose.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/tiger-pose.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/ImageNet.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/signature.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/coco8-multispectral.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/cityscapes.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/ade20k.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
LOWultralytics/cfg/datasets/TT100K.yaml1# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/licenseCOMMENT
Verbosity Indicators21 hits · 19 pts
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LOWultralytics/models/sam/predict.py1699 # Step 0: clear the input on those frames where this object id has point or mask inputCOMMENT
LOWultralytics/models/sam/predict.py1708 # Step 1: Update the object id mapping (note that it must be done after Step 0,COMMENT
LOWultralytics/models/sam/predict.py2817 # Step 1: run backbone and detector in a distributed manner -- this is done via Sam3ImageOnVideoMultiGPU,COMMENT
LOWultralytics/models/sam/predict.py2826 # Step 2: each GPU propagates its local SAM2 states to get the SAM2 prediction masks.COMMENT
LOWultralytics/models/sam/predict.py2860 # Step 4: based on `tracker_update_plan`, each GPU executes the update w.r.t. its local SAM2 inference statesCOMMENT
LOWultralytics/models/sam/predict.py2869 # Step 5: finally, build the outputs for this frame (it only needs to be done on GPU 0 sinceCOMMENT
LOWultralytics/models/sam/predict.py1722 # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys.COMMENT
LOWultralytics/models/sam/predict.py1738 # Step 3: For packed tensor storage, we index the remaining ids and rebuild the per-object slices.COMMENT
LOWultralytics/models/sam/predict.py2840 # Step 3: based on detection outputs and the propagated SAM2 prediction masks, we make plansCOMMENT
LOWultralytics/models/sam/predict.py2973 # Step 1: propagate the local SAM2 states to get the current frame's predictionCOMMENT
LOWultralytics/models/sam/predict.py2985 # Step 2: all-gather `low_res_masks_local` into `low_res_masks_global`COMMENT
LOWultralytics/models/sam/predict.py3058 # Step 1: make the update plan and resolve heuristics on GPU 0COMMENT
LOWultralytics/models/sam/predict.py3189 # Step 4: Run SAM2 memory encoder on the current frame's prediction masksCOMMENT
LOWultralytics/models/sam/predict.py3207 # Step 4: update the SAM2 metadata based on the update planCOMMENT
LOWultralytics/models/sam/predict.py3332 # Step 1: add new objects from the detector to SAM2 inference statesCOMMENT
LOWultralytics/models/sam/predict.py3345 # Step 2: remove from SAM2 inference states those objects removed by heuristicsCOMMENT
LOWultralytics/models/sam/predict.py3643 # Step 1: log the frame index where each object ID first appearsCOMMENT
LOWultralytics/models/sam/predict.py3667 # Step 2: removed tracks that has not matched with detections for `hotstart_unmatch_thresh` frames with hotstartCOMMENT
LOWultralytics/models/sam/predict.py3695 # Step 3: removed tracks that overlaps with another track for `hotstart_dup_thresh` framesCOMMENT
LOWultralytics/models/sam/modules/sam.py651 # Step 1: condition the visual features of the current frame on previous memoriesCOMMENT
LOWultralytics/models/sam/modules/sam.py778 # Step 2: Concatenate the memories and forward through the transformer encoderCOMMENT
AI Slop Vocabulary7 hits · 14 pts
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MEDIUMpyproject.toml19# For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.comCOMMENT
MEDIUMtests/test_python.py829 # Test smart_value() with comprehensive casesCOMMENT
MEDIUMultralytics/solutions/object_counter.py104 # region's shape; a ~5-frame baseline is robust to tracker jitter where a 1-frame delta is not.COMMENT
MEDIUMultralytics/utils/__init__.py639 """Load YAML file to Python object with robust error handling.STRING
MEDIUMultralytics/utils/autodevice.py12 """Manages NVIDIA GPU information via pynvml with robust error handling.STRING
MEDIUMultralytics/utils/callbacks/platform.py279 """Collect comprehensive environment info using existing ultralytics utilities."""STRING
MEDIUMultralytics/cfg/trackers/ocsort.yaml7tracker_type: ocsort # (str) Tracker backend: ocsort; observation-centric SORT for robust multi-object trackingCODE
Hallucination Indicators1 hit · 10 pts
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CRITICALultralytics/utils/export/coreml.py150 pipeline.spec.description.metadata.userDefined.update(CODE
Decorative Section Separators2 hits · 6 pts
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MEDIUMultralytics/models/sam/modules/tiny_encoder.py3# --------------------------------------------------------COMMENT
MEDIUMultralytics/models/sam/modules/tiny_encoder.py10# --------------------------------------------------------COMMENT
Fake / Example Data3 hits · 3 pts
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LOWdocs/en/platform/api/index.md1698 "name": "Jane Doe",CODE
LOWdocs/en/platform/api/index.md1700 "line1": "123 Main St",CODE
LOWdocs/en/platform/api/index.md1956 "email": "user@example.com",CODE
Example Usage Blocks1 hit · 2 pts
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LOWultralytics/hub/google/__init__.py171# Usage exampleCOMMENT
Overly Generic Function Names2 hits · 0 pts
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LOWultralytics/utils/__init__.py538 ... def my_function():STRING
LOWultralytics/utils/__init__.py1173 ... def process_data(data):STRING