Repository Analysis

roboflow/rf-detr

RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning. [ICLR 2026]

70.4 Strong AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of roboflow/rf-detr, a Python project with 8,529 GitHub stars. SynthScan v2.0 examined 84,673 lines of code across 325 source files, recording 3438 pattern matches distributed across 17 syntactic categories. The overall adjusted score of 70.4 places this repository in the Strong 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).

70.4
Adjusted Score
70.4
Raw Score
100%
Time Factor
2026-07-14
Last Push
8.5K
Stars
Python
Language
84.7K
Lines of Code
325
Files
3.4K
Pattern Hits
2026-07-14
Scan Date
0.32
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

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.

No multi-scan history yet — run the scanner again to build trend data.

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 4HIGH 101MEDIUM 928LOW 2405

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 3438 distinct pattern matches across 17 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.

Decorative Section Separators916 hits · 3273 pts
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MEDIUMAGENTS.md147# ------------------------------------------------------------------------COMMENT
MEDIUMAGENTS.md151# ------------------------------------------------------------------------COMMENT
MEDIUMtests/run_smoke_all_models.py2# ------------------------------------------------------------------------COMMENT
MEDIUMtests/run_smoke_all_models.py6# ------------------------------------------------------------------------COMMENT
MEDIUMtests/conftest.py1# ------------------------------------------------------------------------COMMENT
MEDIUMtests/conftest.py5# ------------------------------------------------------------------------COMMENT
MEDIUMtests/__init__.py1# ------------------------------------------------------------------------COMMENT
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MEDIUMtests/training/test_metrics_csv.py1# ------------------------------------------------------------------------COMMENT
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MEDIUMtests/training/test_auto_batch.py1# ------------------------------------------------------------------------COMMENT
MEDIUMtests/training/test_auto_batch.py5# ------------------------------------------------------------------------COMMENT
MEDIUMtests/training/conftest.py1# ------------------------------------------------------------------------COMMENT
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MEDIUMtests/training/test_trainer_smoke.py1# ------------------------------------------------------------------------COMMENT
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MEDIUMtests/training/test_trainer_smoke.py335# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/training/test_trainer_smoke.py337# ---------------------------------------------------------------------------COMMENT
MEDIUMtests/training/test_detr_shim.py90# ---------------------------------------------------------------------------COMMENT
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MEDIUMtests/training/test_detr_shim.py457# ---------------------------------------------------------------------------COMMENT
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MEDIUMtests/training/test_detr_shim.py647# ---------------------------------------------------------------------------COMMENT
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MEDIUMtests/training/test_detr_shim.py1# ------------------------------------------------------------------------COMMENT
MEDIUMtests/training/test_detr_shim.py5# ------------------------------------------------------------------------COMMENT
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Hyper-Verbose Identifiers2012 hits · 1710 pts
SeverityFileLineSnippetContext
LOWtests/run_smoke_all_models.py171def _test_coco_class_name_mapping(model_instance: object) -> None:CODE
LOWtests/conftest.py53def reset_build_namespace_warning_state() -> Generator[None, Any, None]:CODE
LOWtests/conftest.py76def synthetic_shape_dataset_dir(tmp_path_factory: pytest.TempPathFactory) -> Generator[Path, Any, None]:CODE
LOWtests/conftest.py113def synthetic_shape_segmentation_dataset_dir(CODE
LOWtests/training/test_metrics_csv.py101 def test_base_metrics_present_without_ema(self, base_model_config, base_train_config):CODE
LOWtests/training/test_metrics_csv.py113 def test_ema_metrics_present_with_ema_enabled(self, base_model_config, base_train_config):CODE
LOWtests/training/test_metrics_csv.py125 def test_train_loss_is_unscaled(self, base_model_config, base_train_config):CODE
LOWtests/training/test_auto_batch.py25def test_recommend_grad_accum_steps_rounds_up():CODE
LOWtests/training/test_auto_batch.py29def test_probe_max_micro_batch_uses_exponential_then_binary_search():CODE
LOWtests/training/test_auto_batch.py55def test_probe_max_micro_batch_raises_if_one_is_not_safe():CODE
LOWtests/training/test_auto_batch.py74def test_probe_step_raises_when_loss_keys_do_not_overlap_weight_keys():CODE
LOWtests/training/test_auto_batch.py110def test_resolve_auto_batch_config_requires_cuda():CODE
LOWtests/training/test_auto_batch.py122def test_resolve_auto_batch_config_returns_expected_values():CODE
LOWtests/training/test_auto_batch.py150def test_train_auto_batch_ensures_model_on_device_before_resolve(CODE
LOWtests/training/test_auto_batch.py200def test_probe_step_with_real_segmentation_criterion(tmp_path):CODE
LOWtests/training/conftest.py99def _restore_rfdetr_module_trainer_property():CODE
LOWtests/training/conftest.py119def _restore_rfdetr_datamodule_trainer_property():CODE
LOWtests/training/test_trainer_smoke.py61 def test_fit_runs_without_error(self, base_model_config, base_train_config):CODE
LOWtests/training/test_trainer_smoke.py87 def test_training_step_called_expected_times(self, base_model_config, base_train_config):CODE
LOWtests/training/test_trainer_smoke.py124 def test_val_step_called_expected_times(self, base_model_config, base_train_config):CODE
LOWtests/training/test_trainer_smoke.py161 def test_loss_decreases_or_is_finite(self, base_model_config, base_train_config):CODE
LOWtests/training/test_trainer_smoke.py206 def test_fit_runs_without_error(self, base_model_config, seg_train_config):CODE
LOWtests/training/test_trainer_smoke.py232 def test_segmentation_config_accepted(self, base_model_config, seg_train_config):CODE
LOWtests/training/test_trainer_smoke.py263 def test_fit_via_build_trainer(self, base_model_config, base_train_config):CODE
LOWtests/training/test_trainer_smoke.py348 def test_mutation_persists_to_training_step(self, base_model_config, base_train_config):CODE
LOWtests/training/test_trainer_smoke.py371def test_ddp_spawn_fit_runs_without_error(base_model_config, base_train_config):CODE
LOWtests/training/test_trainer_smoke.py402def test_ddp_spawn_multi_scale_mutation_propagates(base_model_config, base_train_config):CODE
LOWtests/training/test_detr_shim.py98 def test_build_trainer_called_with_config_and_model_config(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py108 def test_trainer_fit_called_with_module_and_datamodule(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py120 def test_ckpt_path_none_when_resume_not_set(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py130 def test_ckpt_path_forwarded_when_resume_set(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py140 def test_ckpt_path_none_when_resume_is_empty_string(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py281 def test_device_kwarg_cpu_no_warning(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py291 def test_device_kwarg_cuda_forwards_gpu_accelerator_without_devices(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py301 def test_device_kwarg_torch_device_cuda_index_forwards_gpu_accelerator_and_devices(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py311 def test_callbacks_none_no_warning(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py320 def test_callbacks_empty_dict_no_warning(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py329 def test_callbacks_all_empty_lists_no_warning(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py339 def test_callbacks_non_empty_emits_deprecation_warning(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py385 def test_do_benchmark_not_forwarded_to_get_train_config(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py394 def test_device_not_forwarded_to_get_train_config(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py403 def test_skip_best_epochs_forwarded_to_get_train_config(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py412 def test_batch_size_auto_resolved_before_module_and_datamodule_build(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py465 def test_device_cpu_absorbed_as_accelerator_cpu(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py474 def test_device_cuda_absorbed_as_accelerator_gpu(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py484 def test_device_cuda_index_absorbed_as_accelerator_gpu_devices_list(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py493 def test_device_torch_device_cuda_index_absorbed_as_accelerator_gpu_devices_list(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py502 def test_device_invalid_raises_value_error_with_expected_message(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py514 def test_device_unmapped_valid_type_warns_and_falls_back_to_auto_detection(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py524 def test_callbacks_empty_dict_no_error(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py531 def test_callbacks_non_empty_emits_deprecation_warning(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py655 def test_updates_model_config_resolution(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py665 def test_does_not_implicitly_update_positional_encoding_size(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py691 def test_does_not_reach_get_train_config(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py700 def test_indivisible_raises_value_error(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py709 def test_none_leaves_model_config_unchanged(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py839 def test_epoch_defaults_to_zero_when_missing(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py848 def test_global_step_always_zero(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py856 def test_legacy_checkpoint_format_flag_set(self, tmp_path, patch_lit):CODE
LOWtests/training/test_detr_shim.py864 def test_args_as_namespace_converted_to_dict(self, tmp_path, patch_lit):CODE
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Docstring Block Structure96 hits · 480 pts
SeverityFileLineSnippetContext
HIGHtests/datasets/test_keypoint_schema.py31Write a minimal COCO annotation file. Args: path: Destination JSON path. categories: COCO category STRING
HIGHtests/models/test_model.py14Return the patch-embedding projection layer for an RF-DETR model. RFDETR wrappers are not nn.Module; the underlyingSTRING
HIGHtests/models/heads/test_detection_head.py26Construct the smallest viable LWDETR without loading pretrained weights. Uses a MagicMock backbone and transformer STRING
HIGHtests/models/heads/test_detection_head.py61Construct a minimal keypoint-capable LWDETR with detection head resized to num_classes+1. Mirrors what happens afteSTRING
HIGHtests/models/heads/test_detection_head.py104Build a zero keypoint prediction tensor with the shape expected by _aggregate_keypoint_class_logits. The second-to-STRING
HIGHtests/benchmarks/test_inference_coco.py288Load a deterministic subset of COCO person-keypoint validation images. Args: images_root: Directory containSTRING
HIGHtests/benchmarks/test_inference_coco.py330Run keypoint-preview inference in fixed-size batches. Args: model: Loaded keypoint-preview model. iSTRING
HIGHtests/export/test_onnx_notes.py37Export a tiny model to ONNX and return the output file path. Args: tmp_path: Temporary directory provided bSTRING
HIGHdocs/hooks/cookbooks_cards.py15Load cookbook card definitions from a YAML file. Reads the YAML file and returns the ``cards`` list, which is consuSTRING
HIGHdocs/hooks/cookbooks_cards.py47Expose cookbook card data to MkDocs templates. Adds ``config.extra.cookbooks_cards`` so the cookbooks landing-page STRING
HIGHdocs/hooks/package_version.py19Load project metadata from a pyproject file. Reads the TOML file in binary mode, matching the parser API. The returSTRING
HIGHdocs/hooks/package_version.py47Read the package version from pyproject project metadata. Validates that the version exists and is a string before STRING
HIGHdocs/hooks/package_version.py74Expose the package version to MkDocs templates. Adds ``config.extra.software_version`` so schema.org JSON-LD can stSTRING
HIGH.github/CONTRIBUTING.md428 Provides a brief description of function behavior. Args: param1: Explanation of the first parameter's STRING
HIGHsrc/rfdetr/detr.py113Validate a user-supplied ``(height, width)`` shape tuple and return normalised plain-int dims. Args: shape:STRING
HIGHsrc/rfdetr/detr.py157Resolve and validate the ``patch_size`` argument for :meth:`RFDETR.export` and :meth:`RFDETR.predict`. Args: STRING
HIGHsrc/rfdetr/detr.py310Load an RF-DETR model from a training checkpoint, automatically inferring the model class. The correct subclassSTRING
HIGHsrc/rfdetr/detr.py640Map a torch-style device specifier to PTL ``accelerator``/``devices`` kwargs. Args: device: A devicSTRING
HIGHsrc/rfdetr/detr.py926Optimize the model for inference with optional JIT compilation and dtype casting. Operations are wrapped in theSTRING
HIGHsrc/rfdetr/detr.py1196Export the trained model to ONNX or TFLite format. See the `export documentation <https://rfdetr.roboflow.com/lSTRING
HIGHsrc/rfdetr/detr.py1557Return the Roboflow COCO train annotation path when it exists. Args: dataset_dir: Path to the RobofSTRING
HIGHsrc/rfdetr/detr.py1579Return the native COCO train keypoint annotation path when it exists. Args: dataset_dir: Path to thSTRING
HIGHsrc/rfdetr/detr.py1599Return the YOLO data file path when a dataset root has one. Args: dataset_dir: Path to the YOLO datSTRING
HIGHsrc/rfdetr/detr.py1636Infer or validate keypoint schema from COCO, Roboflow COCO, or YOLO pose metadata. Args: config: TrSTRING
HIGHsrc/rfdetr/detr.py1808Performs model inference on the input images. This method accepts a single image or a list of images in variousSTRING
HIGHsrc/rfdetr/config.py438Normalize supported device inputs to a canonical torch-style string. Args: v: Device specifier provSTRING
HIGHsrc/rfdetr/util/io.py29Load a PyTorch checkpoint as safely as possible. Tries progressively less restrictive deserialization strategies: STRING
HIGHsrc/rfdetr/training/module_data.py128Resolve ``"auto"`` to ``"cpu"`` or ``"gpu"`` based on runtime availability. For ``"cpu"`` and ``"gpu"`` the value iSTRING
HIGHsrc/rfdetr/training/module_data.py401Build a private diagnostic figure for transformed dataset samples. Samples the dataset after RF-DETR dataset trSTRING
HIGHsrc/rfdetr/training/drop_schedule.py26Build a per-iteration drop-path or dropout rate schedule. ``"standard"`` mode: every iteration uses the same ``dropSTRING
HIGHsrc/rfdetr/training/auto_batch.py171Find the largest per-device batch size that fits in memory for one train step. Uses exponential search (1, 2, 4, ..STRING
HIGHsrc/rfdetr/training/auto_batch.py277Recommend gradient accumulation steps to reach target effective batch size. Args: safe_micro_batch: Per-steSTRING
HIGHsrc/rfdetr/training/auto_batch.py303Resolve batch_size='auto' into concrete batch_size and grad_accum_steps using a probe. Expects model_context to havSTRING
HIGHsrc/rfdetr/datasets/synthetic.py69Normalize split ratios parameter to a dictionary. Args: split_ratios: Can be: - DatasetSplitRatSTRING
HIGHsrc/rfdetr/datasets/coco.py531Build the standard COCO transform pipeline for a given dataset split. Returns a composed transform that resizes imaSTRING
HIGHsrc/rfdetr/datasets/transforms.py51Normalize image and convert target coordinates to relative format. Applies ImageNet-style channel normalizationSTRING
HIGHsrc/rfdetr/datasets/transforms.py153Return True if transform (or any nested transform) affects spatial coordinates. For container transforms such as ``STRING
HIGHsrc/rfdetr/datasets/transforms.py182Build a single Albumentations transform from its name and parameter dict. Handles container transforms (``OneOf``, STRING
HIGHsrc/rfdetr/datasets/transforms.py278Normalize transform params across Albumentations API variations. Currently this adapts ``RandomSizedCrop`` argumentSTRING
HIGHsrc/rfdetr/datasets/transforms.py784Apply the Albumentations transform to image and target. This method handles the data format conversion between STRING
HIGHsrc/rfdetr/datasets/transforms.py886Build a list of :class:`AlbumentationsWrapper` instances from a config. Supports both a flat dictionary format STRING
HIGHsrc/rfdetr/datasets/__init__.py44Detect if a Roboflow dataset is in COCO or YOLO format. Args: dataset_dir: Path to the Roboflow dataset rooSTRING
HIGHsrc/rfdetr/datasets/kornia_transforms.py87Resolve an ``augmentation_backend`` value to a concrete ``"cpu"`` or ``"gpu"``. ``"auto"`` resolves to ``"gpu"`` onSTRING
HIGHsrc/rfdetr/datasets/kornia_transforms.py303Build a Kornia ``AugmentationSequential`` from an aug_config dict. Each key in *aug_config* is looked up in ``_REGISTRING
HIGHsrc/rfdetr/datasets/kornia_transforms.py441Pack variable-length instance masks into a zero-padded ``[B, N_max, H, W]`` tensor. Kornia ``AugmentationSequentialSTRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py32Keypoint schema inferred from COCO category metadata. Args: class_names: Category names sorted by category STRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py59Keypoint schema inferred from an Ultralytics YOLO pose YAML file. Args: class_names: Class names ordered bySTRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py94Load a YAML file and require a mapping root. Args: yaml_path: Path to a YAML data file. Returns: STRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py289Load a COCO annotation JSON file. Args: annotation_path: Path to a COCO annotation JSON file. Returns:STRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py316Validate and sort COCO categories by category id. Args: categories: Raw ``categories`` value from a COCO anSTRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py347Validate COCO annotations container type. Args: annotations: Raw ``annotations`` value from a COCO annotatiSTRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py371Infer keypoint count for one category from annotation vectors. Args: annotations: COCO annotation dictionarSTRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py411Infer a keypoint schema from a COCO annotation JSON file. Args: annotation_path: Path to a COCO annotation STRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py467Infer a keypoint schema from an Ultralytics YOLO pose YAML file. Args: data_file: Path to a YOLO ``data.yamSTRING
HIGHsrc/rfdetr/datasets/_keypoint_schema.py507Return non-zero keypoint counts from a model schema. Args: num_keypoints_per_class: Model keypoint schema. STRING
HIGHsrc/rfdetr/datasets/yolo.py183Parse one YOLO label line and return ``(class_id, xyxy_px, polygon_px)``. Args: values: Whitespace-split fiSTRING
HIGHsrc/rfdetr/datasets/yolo.py352Build the class list and sample list shared by both YOLO builder functions. Iterates over every image in ``img_foldSTRING
HIGHsrc/rfdetr/datasets/yolo.py636Parse ``data_file`` and resolve image/label dirs for ``split``. Returns ``None`` when the YAML declares no usable pSTRING
HIGHsrc/rfdetr/datasets/_develop.py44Check whether the COCO val2017 image directory contains the expected number of JPEG files. Returns ``False`` for a STRING
HIGHsrc/rfdetr/datasets/_develop.py76Check whether a file exists and contains at least one byte. Returns ``False`` for a missing or empty file so callerSTRING
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Unused Imports221 hits · 208 pts
SeverityFileLineSnippetContext
LOWtests/training/test_metrics_csv.py16CODE
LOWtests/training/test_cli.py25CODE
LOWtests/training/helpers.py14CODE
LOWtests/training/callbacks/test_ema_callback.py8CODE
LOWtests/training/callbacks/test_best_model_callback.py8CODE
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Hallucination Indicators4 hits · 60 pts
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CRITICALtests/models/backbone/test_windowed_dino.py482 before = [layer.attention.attention.query.weight.clone() for layer in model.encoder.layer]CODE
CRITICALtests/models/backbone/test_windowed_dino.py497 key_before = [layer.attention.attention.key.weight.clone() for layer in model.encoder.layer]CODE
CRITICALtests/models/backbone/test_windowed_dino.py498 val_before = [layer.attention.attention.value.weight.clone() for layer in model.encoder.layer]CODE
CRITICALtests/models/backbone/test_windowed_dino.py515 dense_before = [layer.attention.output.dense.weight.clone() for layer in model.encoder.layer]CODE
Over-Commented Block48 hits · 46 pts
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LOWmkdocs.yaml81theme:COMMENT
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LOWtests/datasets/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWtests/models/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWtests/models/heads/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWtests/models/backbone/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWtests/cli/__init__.py1# ------------------------------------------------------------------------COMMENT
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LOWtests/inference/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWtests/evaluation/__init__.py1# ------------------------------------------------------------------------COMMENT
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LOW.github/workflows/publish-docs.yml1# Build and deploy MkDocs documentation to GitHub Pages (https://rfdetr.roboflow.com/)COMMENT
LOWsrc/rfdetr/detr.py601 # a caller accidentally passes pretrain_weights inside kwargs.COMMENT
LOWsrc/rfdetr/util/misc.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/util/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/training/module_model.py101 if compile_enabled:COMMENT
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LOWsrc/rfdetr/training/trainer.py61# "Invalid thread pool!" SIGABRT when the autograd engine initialises inCOMMENT
LOWsrc/rfdetr/training/trainer.py181 f"amp_dtype={tc.amp_dtype!r} has no effect when model_config.amp=False.",COMMENT
LOWsrc/rfdetr/datasets/coco.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/datasets/transforms.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/datasets/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/models/matcher.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/models/position_encoding.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/models/postprocess.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/models/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/models/transformer.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/models/math.py1# ------------------------------------------------------------------------COMMENT
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LOWsrc/rfdetr/models/ops/__init__.py1# ------------------------------------------------------------------------COMMENT
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LOWsrc/rfdetr/models/ops/functions/__init__.py1# ------------------------------------------------------------------------COMMENT
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LOWsrc/rfdetr/evaluation/matching.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/export/__init__.py1# ------------------------------------------------------------------------COMMENT
LOWsrc/rfdetr/export/_tflite/converter.py901 # _patch_validation_download redirects onnx2tf'sCOMMENT
Modern Structural Boilerplate37 hits · 44 pts
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LOWtests/training/test_detr_shim.py1531 def _set_class_names(mock_self: MagicMock, class_names: list[str]) -> None:CODE
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LOWtests/training/helpers.py38 def update_dropout(self, *args, **kwargs) -> None:CODE
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LOWsrc/rfdetr/training/module_data.py572 def _setup_kornia_pipeline(self) -> None:CODE
LOWsrc/rfdetr/training/checkpoint.py22logger = logging.getLogger(__name__)CODE
LOWsrc/rfdetr/training/checkpoint.py24__all__ = ["convert_legacy_checkpoint"]CODE
LOWsrc/rfdetr/training/model_ema.py138__all__ = [CODE
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LOWsrc/rfdetr/datasets/_keypoint_schema.py20__all__ = [CODE
LOWsrc/rfdetr/models/__init__.py24__all__ = [CODE
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LOWsrc/rfdetr/models/lwdetr.py745 def update_drop_path(self, drop_path_rate: float, vit_encoder_num_layers: int) -> None:CODE
LOWsrc/rfdetr/models/lwdetr.py764 def update_dropout(self, drop_rate: float) -> None:CODE
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LOWsrc/rfdetr/models/backbone/dinov2_with_windowed_attn.py969 def set_attn_implementation(self, attn_implementation: str) -> None: # type: ignore[override]STRING
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LOWsrc/rfdetr/export/_onnx/exporter.py319 def set_severity(self, severity: object) -> None:CODE
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Deep Nesting37 hits · 32 pts
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LOWsrc/rfdetr/evaluation/coco_eval.py420CODE
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LOWsrc/rfdetr/evaluation/matching.py134CODE
LOWsrc/rfdetr/export/benchmark.py390CODE
LOWsrc/rfdetr/export/main.py94CODE
LOWsrc/rfdetr/export/_onnx/exporter.py550CODE
LOWsrc/rfdetr/export/_onnx/exporter.py616CODE
LOWsrc/rfdetr/export/_tflite/converter.py680CODE
Magic Placeholder Names5 hits · 25 pts
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HIGHdocs/learn/deploy.md30 api_key="<YOUR_API_KEY>",CODE
HIGHdocs/learn/deploy.md30 api_key="<YOUR_API_KEY>",CODE
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HIGHdocs/learn/deploy.md44 api_key="<YOUR_API_KEY>",CODE
HIGHdocs/root-static/llms-full.txt237 api_key="YOUR_API_KEY"CODE
Self-Referential Comments8 hits · 22 pts
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MEDIUMtests/training/callbacks/test_coco_eval_callback.py466 # Create a fake callback whose class name is RichProgressBar soCOMMENT
MEDIUMtests/assets/test_downloads.py192 # Create a mock asset without MD5COMMENT
MEDIUMtests/assets/test_downloads.py206 # Create a mock asset without MD5COMMENT
MEDIUMsrc/rfdetr/datasets/yolo.py785 >>> # Create a sample image and targetSTRING
MEDIUMsrc/rfdetr/models/backbone/dinov2.py84 # Create the encoderCOMMENT
MEDIUMsrc/rfdetr/models/backbone/dinov2.py203 # Create a new Parameter with the new sizeCOMMENT
MEDIUMsrc/rfdetr/models/backbone/projector.py189 """This module implements MultiScaleProjector in :paper:`lwdetr`.STRING
MEDIUMsrc/rfdetr/utilities/state_dict.py180 # Create the temp file in the destination directory so os.replace stays on the same filesystem (atomic).COMMENT
Example Usage Blocks11 hits · 16 pts
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LOWconfigs/rfdetr_seg_small.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_seg_2xlarge.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_seg_large.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_seg_nano.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_seg_xlarge.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_base.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_medium.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_seg_medium.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_small.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_nano.yaml2# Usage:COMMENT
LOWconfigs/rfdetr_large.yaml2# Usage:COMMENT
AI Structural Patterns16 hits · 14 pts
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LOWtests/export/test_export.py249CODE
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LOWsrc/rfdetr/models/transformer.py519CODE
LOWsrc/rfdetr/models/transformer.py599CODE
LOWsrc/rfdetr/models/transformer.py755CODE
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LOWsrc/rfdetr/models/backbone/dinov2.py59CODE
LOWsrc/rfdetr/models/backbone/backbone.py39CODE
Excessive Try-Catch Wrapping13 hits · 12 pts
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LOWtests/run_smoke_all_models.py275 except Exception as ex:CODE
LOWtests/conftest.py42 except Exception as exc:CODE
LOWsrc/rfdetr/detr.py52except Exception:CODE
LOWsrc/rfdetr/detr.py867 except Exception:CODE
LOWsrc/rfdetr/detr.py1073 except Exception:CODE
LOWsrc/rfdetr/variants.py203 except Exception as retry_exc:CODE
LOWsrc/rfdetr/datasets/transforms.py1006 except Exception as e:CODE
LOWsrc/rfdetr/models/weights.py332 except Exception:CODE
LOWsrc/rfdetr/utilities/files.py110 except Exception:CODE
LOWsrc/rfdetr/utilities/files.py128 except Exception:CODE
LOWsrc/rfdetr/export/_tflite/converter.py463 except Exception as exc:CODE
LOWsrc/rfdetr/export/_tflite/converter.py644 except Exception:CODE
LOWsrc/rfdetr/export/_tflite/converter.py957 except Exception as exc:CODE
AI Slop Vocabulary4 hits · 9 pts
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LOWtests/training/test_drop_path.py139 # Should not raise an error, just return earlyCOMMENT
MEDIUMtests/inference/test_predict.py1070 """``predict()`` URL handling: robust HTTP fetch and correct local-path classification."""STRING
MEDIUMsrc/rfdetr/export/_onnx/inference.py180 # Match by name so the code is robust to output reordering.COMMENT
MEDIUMsrc/rfdetr/export/_tflite/inference.py253 # Match by name so the code is robust to onnx2tf output reordering.COMMENT
Modern AI Meta-Vocabulary1 hit · 4 pts
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MEDIUMtests/export/test_export.py675 # patch_size=14, num_windows=3 → block_size=42; scaffold sets resolution=84 (42*2) which is validCOMMENT
Fake / Example Data7 hits · 4 pts
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LOWtests/training/test_detr_shim.py1564 api_key="dummy-key",CODE
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LOWtests/training/test_detr_shim.py1729 api_key="dummy-key",CODE
Redundant / Tautological Comments2 hits · 3 pts
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LOWsrc/rfdetr/utilities/files.py71 # Check if file exists and has correct hashCOMMENT
LOWsrc/rfdetr/assets/model_weights.py416 # Check if we have a hash for this modelCOMMENT