Repository Analysis

tensorflow/examples

TensorFlow examples

12.7 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of tensorflow/examples, a Jupyter Notebook project with 8,281 GitHub stars. SynthScan v2.0 examined 168,678 lines of code across 1367 source files, recording 1541 pattern matches distributed across 18 syntactic categories. The overall adjusted score of 12.7 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

12.7
Adjusted Score
12.7
Raw Score
100%
Time Factor
2026-08-26
Last Push
8.3K
Stars
Jupyter Notebook
Language
168.7K
Lines of Code
1.4K
Files
1.5K
Pattern Hits
2026-08-29
Scan Date
0.05
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 1HIGH 65MEDIUM 166LOW 1309

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 1541 distinct pattern matches across 18 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.

Over-Commented Block515 hits · 515 pts
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LOWlite/examples/recommendation/ml/data/__init__.py1# Copyright 2021 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…mendation/ml/data/example_generation_movielens_test.py1# Copyright 2021 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…recommendation/ml/data/example_generation_movielens.py1# Copyright 2021 The TensorFlow Authors. All Rights Reserved.COMMENT
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LOW…e/examples/bert_qa/ios/BertQACore/Models/Dataset.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…a/ios/BertQACore/Models/Tokenizers/FullTokenizer.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…/BertQACore/Models/Tokenizers/WordpieceTokenizer.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…/ios/BertQACore/Models/Tokenizers/BasicTokenizer.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
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LOW…examples/bert_qa/ios/BertQACore/Models/ML/Result.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…s/bert_qa/ios/BertQACore/Models/ML/InputFeatures.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…les/bert_qa/ios/BertQACore/Models/ML/ContentData.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…ios/BertQACore/Extensions/UnicodeScalarExtension.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…/bert_qa/ios/BertQACore/Extensions/DataExtension.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…ert_qa/ios/BertQACore/Extensions/StringExtension.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…ert_qa/ios/BertQACore/Extensions/StringExtension.swift41 func splitByWhitespace() -> [String] {COMMENT
LOW…bert_qa/ios/ViewInSwiftUI/KeyboardHeightObserver.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…e/examples/bert_qa/ios/ViewInSwiftUI/AppDelegate.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…examples/bert_qa/ios/ViewInSwiftUI/SceneDelegate.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…ert_qa/ios/ViewInSwiftUI/Views/DatasetDetailView.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…a/ios/ViewInSwiftUI/Views/SuggestedQuestionsView.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
LOW…/bert_qa/ios/ViewInSwiftUI/Views/DatasetListView.swift1// Copyright 2020 The TensorFlow Authors. All Rights Reserved.COMMENT
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Unused Imports457 hits · 454 pts
SeverityFileLineSnippetContext
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Decorative Section Separators135 hits · 412 pts
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Hyper-Verbose Identifiers240 hits · 239 pts
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LOW…s/recommendation/ml/model/recommendation_model_test.py24 def _create_test_input_config(self,CODE
LOW…s/recommendation/ml/model/recommendation_model_test.py50 def _create_test_model_config(self):CODE
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LOWlite/examples/recommendation/ml/model/input_pipeline.py109def get_features_and_vocabs_by_name(CODE
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LOW…amples/recommendation/ml/model/context_encoder_test.py39 def _create_test_input_config(self):CODE
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LOW…amples/recommendation/ml/model/context_encoder_test.py73 def test_feature_group_encoder_bow(self):CODE
LOW…amples/recommendation/ml/model/context_encoder_test.py88 def test_feature_group_encoder_cnn(self):CODE
LOW…amples/recommendation/ml/model/context_encoder_test.py103 def test_feature_group_encoder_lstm(self):CODE
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LOW…recommendation/ml/data/example_generation_movielens.py334def generate_movie_feature_vocabs(movies_df, movie_counts):CODE
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LOWlite/examples/pose_estimation/raspberry_pi/data.py70def person_from_keypoints_with_scores(CODE
LOW…te/examples/pose_estimation/raspberry_pi/visualizer.py48def _visualize_detection_result(input_image, ground_truth):CODE
LOW…tion/raspberry_pi/tracker/bounding_box_tracker_test.py62 def test_bounding_box_tracker(self):CODE
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LOW…amples/pose_estimation/raspberry_pi/tracker/tracker.py184 def _update_and_get_next_track_id(self):CODE
LOW…te/examples/pose_estimation/raspberry_pi/ml/movenet.py132 def _determine_torso_and_body_range(self, keypoints: np.ndarray,CODE
LOW…Classification/ModelDataHandler/ModelDataHandler.swift52class ModelDataHandler {CODE
LOW…SpeechCommands/ModelDataHandler/ModelDataHandler.swift36class ModelDataHandler {CODE
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LOW…et/ios/PoseNet/ModelDataHandler/ModelDataHandler.swift23class ModelDataHandler {CODE
LOW…ite/model_maker/demo/audio_classification_demo_test.py61 def test_audio_classification_demo(self, spec, dataset):CODE
LOW…ite/model_maker/demo/image_classification_demo_test.py50 def test_image_classification_demo(self):CODE
LOW…lite/model_maker/demo/text_classification_demo_test.py50 def test_text_classification_demo(self):CODE
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LOW…core/data_util/object_detector_dataloader_util_test.py38 def test_filename_from_pascal(self):CODE
LOW…core/data_util/object_detector_dataloader_util_test.py102 def test_pascal_voc_cache_writer(self):CODE
LOW…ite/model_maker/core/data_util/text_dataloader_test.py55 def convert_examples_to_features(self, examples, tfrecord_file, label_names):CODE
LOW…ite/model_maker/core/data_util/text_dataloader_test.py132 def convert_examples_to_features(self, examples, is_training, output_fn,CODE
LOW…maker/core/data_util/recommendation_dataloader_test.py30 def test_download_and_extract_data(self):CODE
LOW…maker/core/data_util/recommendation_dataloader_test.py37 def test_generate_movielens_dataset(self):CODE
LOW…les/lite/model_maker/core/data_util/text_dataloader.py353 def _generate_tf_record_from_squad_file(cls,CODE
LOW…es/lite/model_maker/core/data_util/audio_dataloader.py111 def examples_and_label_indices(self):CODE
LOW…es/lite/model_maker/core/data_util/audio_dataloader.py124 def examples_and_label_indices_ds(self):CODE
LOW…/model_maker/core/data_util/recommendation_testutil.py105def patch_download_and_extract_data(data_dir):CODE
LOW…aker/core/data_util/object_detector_dataloader_util.py60def get_cache_prefix_filename_from_pascal(images_dir: str,CODE
LOW…aker/core/data_util/object_detector_dataloader_util.py88def get_cache_prefix_filename_from_csv(csv_file: str, num_shards: int) -> str:CODE
LOW…aker/core/data_util/object_detector_dataloader_util.py316def _get_xml_dict_from_csv_lines(images_dir: str, image_filename: str,CODE
LOW…odel_maker/core/data_util/recommendation_dataloader.py142 def download_and_extract_movielens(cls, download_dir):CODE
LOW…odel_maker/core/data_util/recommendation_dataloader.py147 def generate_movielens_dataset(CODE
LOW…te/model_maker/core/data_util/audio_dataloader_test.py230 def test_no_audio_files_found(self):CODE
LOW…te/model_maker/core/data_util/audio_dataloader_test.py237 def test_failed_librosa_imoprt(self):CODE
LOW…xamples/lite/model_maker/core/utils/scann_converter.py123def convert_serialized_to_on_device(serialized_path: str) -> OnDeviceArtifacts:CODE
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Docstring Block Structure39 hits · 195 pts
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HIGHlite/examples/recommendation/ml/model/input_pipeline.py147Get feature spec based on feature type and length. Args: feature_type: The type of the feature (input_config_pb2.STRING
HIGHlite/examples/recommendation/ml/model/utils.py30Get a list of filenames given a pattern. This will also check whether the files exist on the filesystem. The pattern STRING
HIGH…recommendation/ml/data/example_generation_movielens.py222Generate TF examples from a single user timeline. Generate TF examples from a single user timeline. Timeline with lenSTRING
HIGH…recommendation/ml/data/example_generation_movielens.py286Convert user timelines to tf examples. Convert user timelines to tf examples by adding all possible context-label pSTRING
HIGH…xamples/lite/model_maker/core/utils/scann_converter.py64Helper util that builds an indexer for `convert_artifacts_to_leveldb`. Args: on_device_distance: DistanceMeasure STRING
HIGH…les/lite/model_maker/core/task/model_spec/text_spec.py1020Evaluate QA model. Args: model: The keras model to be evaluated. tflite_filepath: File path to the TFLiSTRING
HIGH…/third_party/recommendation/ml/model/input_pipeline.py147Get feature spec based on feature type and length. Args: feature_type: The type of the feature (input_config_pb2.STRING
HIGH…del_maker/third_party/recommendation/ml/model/utils.py30Get a list of filenames given a pattern. This will also check whether the files exist on the filesystem. The pattern STRING
HIGH…recommendation/ml/data/example_generation_movielens.py222Generate TF examples from a single user timeline. Generate TF examples from a single user timeline. Timeline with lenSTRING
HIGH…recommendation/ml/data/example_generation_movielens.py286Convert user timelines to tf examples. Convert user timelines to tf examples by adding all possible context-label pSTRING
HIGH…e/model_maker/third_party/efficientdet/det_model_fn.py302Model definition entry. Args: features: the input image tensor with shape [batch_size, height, width, 3]. TSTRING
HIGH…el_maker/third_party/efficientdet/efficientdet_arch.py308Builds backbone model. Args: features: input tensor. config: config for backbone, such as is_training_bn and baSTRING
HIGH…lite/model_maker/third_party/efficientdet/inference.py119Read and preprocess input images. Args: image_path_pattern: a path to indicate a single or multiple files. imSTRING
HIGH…te/model_maker/third_party/efficientdet/coco_metric.py93Evaluates with detections from all images with COCO API. Args: log_level: Logging lavel to print logs. ReSTRING
HIGH…d_party/efficientdet/dataset/create_pascal_tfrecord.py106Convert XML derived dict to tf.Example proto. Notice that this function normalizes the bounding box coordinates proviSTRING
HIGH…ird_party/efficientdet/dataset/create_coco_tfrecord.py72Converts image and annotations to a tf.Example proto. Args: image: dict with keys: [u'license', u'file_name', u'cSTRING
HIGH…aker/third_party/efficientdet/visualize/shape_utils.py178Runs map_fn as a (static) for loop when possible. This function rewrites the map_fn as an explicit unstack input -> fSTRING
HIGH…aker/third_party/efficientdet/visualize/shape_utils.py250Checks that the image width/height are greater than some number. This function is used to check that the width and heSTRING
HIGH…aker/third_party/efficientdet/visualize/shape_utils.py289Asserts that shape_a and shape_b are equal. If the shapes are static, raises a ValueError when the shapes mismatch.STRING
HIGH…aker/third_party/efficientdet/visualize/shape_utils.py318Asserts that shape_a and shape_b are the same along the 0th-dimension. If the shapes are static, raises a ValueError STRING
HIGH…aker/third_party/efficientdet/visualize/shape_utils.py347Asserts the input box tensor is normalized. Args: boxes: a tensor of shape [N, 4] where N is the number of boxes.STRING
HIGH…aker/third_party/efficientdet/visualize/shape_utils.py370Flattens `K-d` tensor along [first, last) dimensions. Converts `inputs` with shape [D0, D1, ..., D(K-1)] into a tensoSTRING
HIGH…party/efficientdet/object_detection/target_assigner.py82Assign classification and regression targets to each anchor. For a given set of anchors and groundtruth detections,STRING
HIGH…ird_party/efficientdet/object_detection/shape_utils.py21Asserts that shape_a and shape_b are equal. If the shapes are static, raises a ValueError when the shapes mismatch.STRING
HIGH…third_party/efficientdet/object_detection/box_coder.py116Decode a batch of encoded boxes. This op takes a batch of encoded bounding boxes and transforms them to a batch of STRING
HIGH…rd_party/efficientdet/object_detection/preprocessor.py119Randomly flips the image and detections horizontally. The probability of flipping the image is 50%. Args: imagSTRING
HIGH…rd_party/efficientdet/object_detection/preprocessor.py281Resizes an image so its dimensions are within the provided value. The output size can be described by two cases: 1.STRING
HIGH…/third_party/efficientdet/object_detection/box_list.py122Accesses a box collection and associated fields. This function returns specified field with object; if no field is STRING
HIGH…/third_party/efficientdet/object_detection/box_list.py187Retrieves specified fields as a dictionary of tensors. Args: fields: (optional) list of fields to return in tSTRING
HIGH…rd_party/efficientdet/backbone/efficientnet_builder.py227A helper function to create a model and return predicted logits. Args: images: input images tensor. model_namSTRING
HIGH…rd_party/efficientdet/backbone/efficientnet_builder.py293Create a base feature network and return the features before pooling. Args: images: input images tensor. modeSTRING
HIGH…rty/efficientdet/backbone/efficientnet_lite_builder.py112A helper function to create a model and return predicted logits. Args: images: input images tensor. model_namSTRING
HIGH…rty/efficientdet/backbone/efficientnet_lite_builder.py176Create a base feature network and return the features before pooling. Args: images: input images tensor. modeSTRING
HIGH…/third_party/efficientdet/backbone/backbone_factory.py36A helper function to create and return model. Args: model_name: string, the predefined model name. override_pSTRING
HIGHtensorflow_examples/models/densenet/densenet.py32Calculate number of layers in each block from the depth. Args: depth: Depth of model num_blocks: Number of deSTRING
HIGHtensorflow_examples/models/densenet/densenet.py64Calculate number of layers in each block. Args: depth: Depth of model num_blocks: Number of dense blocks STRING
HIGHtensorflow_examples/models/densenet/densenet.py91Calculate number of layers in each block. Args: depth: Depth of model num_blocks: Number of dense blocks STRING
HIGHtensorflow_examples/models/nmt_with_attention/utils.py138Create a tf.data Dataset. Args: path_to_file: Path to the file to load the text from. num_examples: Number ofSTRING
HIGH…g/imagenet_preprocessing_ineffecient_input_pipeline.py452Subtracts the given means from each image channel. For example: means = [123.68, 116.779, 103.939] image = _mSTRING
Cross-File Repetition26 hits · 130 pts
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HIGH…xamples/recommendation/ml/model/input_pipeline_test.py0activity_feature_groups { features { feature_name: "context_movie_id" feature_type: int vocab_size: 3952 embedding_dim: STRING
HIGH…d_party/recommendation/ml/model/input_pipeline_test.py0activity_feature_groups { features { feature_name: "context_movie_id" feature_type: int vocab_size: 3952 embedding_dim: STRING
HIGH…ndation/ml/model/recommendation_model_launcher_test.py0activity_feature_groups { features { feature_name: "context_movie_id" feature_type: int vocab_size: 3952 embedding_dim: STRING
HIGH…ndation/ml/model/recommendation_model_launcher_test.py0activity_feature_groups { features { feature_name: "context_movie_id" feature_type: int vocab_size: 3952 embedding_dim: STRING
HIGH…xamples/recommendation/ml/model/input_pipeline_test.py0features { feature { key: "context_movie_id" value { int64_list { value: [1, 2, 3, 0, 0] } } } feature { key: "context_mSTRING
HIGH…ndation/ml/model/recommendation_model_launcher_test.py0features { feature { key: "context_movie_id" value { int64_list { value: [1, 2, 3, 0, 0] } } } feature { key: "context_mSTRING
HIGH…d_party/recommendation/ml/model/input_pipeline_test.py0features { feature { key: "context_movie_id" value { int64_list { value: [1, 2, 3, 0, 0] } } } feature { key: "context_mSTRING
HIGH…ndation/ml/model/recommendation_model_launcher_test.py0features { feature { key: "context_movie_id" value { int64_list { value: [1, 2, 3, 0, 0] } } } feature { key: "context_mSTRING
HIGH…mendation/ml/data/example_generation_movielens_test.py0features { feature { key: "context_movie_id" value { int64_list { value: [1, 2, 3, 0, 0] } } } feature { key: "context_mSTRING
HIGH…mendation/ml/data/example_generation_movielens_test.py0features { feature { key: "context_movie_id" value { int64_list { value: [1, 2, 3, 0, 0] } } } feature { key: "context_mSTRING
HIGH…ation/metadata/metadata_writer_for_image_classifier.py0populates the metadata and label file to the model file.STRING
HIGH…aker/core/task/metadata_writer_for_image_classifier.py0populates the metadata and label file to the model file.STRING
HIGH…el_maker/core/task/metadata_writers/metadata_writer.py0populates the metadata and label file to the model file.STRING
HIGH…ite/model_maker/demo/audio_classification_demo_test.py0patch to train partial dataset rather than all of them.STRING
HIGH…ite/model_maker/demo/image_classification_demo_test.py0patch to train partial dataset rather than all of them.STRING
HIGH…lite/model_maker/demo/text_classification_demo_test.py0patch to train partial dataset rather than all of them.STRING
HIGH…xamples/lite/model_maker/core/task/image_classifier.py0converts the retrained model to tflite format and saves it. args: tflite_filepath: file path to save tflite model. quantSTRING
HIGH…examples/lite/model_maker/core/task/object_detector.py0converts the retrained model to tflite format and saves it. args: tflite_filepath: file path to save tflite model. quantSTRING
HIGH…examples/lite/model_maker/core/task/text_classifier.py0converts the retrained model to tflite format and saves it. args: tflite_filepath: file path to save tflite model. quantSTRING
HIGH…examples/lite/model_maker/core/task/question_answer.py0converts the retrained model to tflite format and saves it. args: tflite_filepath: file path to save tflite model. quantSTRING
HIGH…mples/lite/model_maker/core/task/model_util_v1_test.py0share text tests of the base class, but in tf v1 behavior.STRING
HIGH…/lite/model_maker/core/task/text_classifier_v1_test.py0share text tests of the base class, but in tf v1 behavior.STRING
HIGH…/lite/model_maker/core/task/question_answer_v1_test.py0share text tests of the base class, but in tf v1 behavior.STRING
HIGH…_examples/models/densenet/densenet_distributed_test.py0densely connected convolutional networks. reference [ densely connected convolutional networks](https://arxiv.org/abs/16STRING
HIGHtensorflow_examples/models/densenet/densenet.py0densely connected convolutional networks. reference [ densely connected convolutional networks](https://arxiv.org/abs/16STRING
HIGHtensorflow_examples/models/densenet/densenet_test.py0densely connected convolutional networks. reference [ densely connected convolutional networks](https://arxiv.org/abs/16STRING
Self-Referential Comments23 hits · 65 pts
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MEDIUM…examples/video_classification/raspberry_pi/classify.py48 # Initialize the video classification modelCOMMENT
MEDIUM…/video_classification/raspberry_pi/video_classifier.py109 # Create the initial (zero) statesCOMMENT
MEDIUM…amples/pose_estimation/raspberry_pi/pose_estimation.py81 # Initialize the classification modelCOMMENT
MEDIUM…te/examples/pose_estimation/raspberry_pi/visualizer.py81 # Create a fake score column to convert ground truth to "Person" typeCOMMENT
MEDIUM…s/pose_estimation/raspberry_pi/ml/movenet_multipose.py166 # Create the list of keypointsCOMMENT
MEDIUM…s/pose_estimation/raspberry_pi/ml/movenet_multipose.py185 # Create a Person instance corresponding to the detected entity.COMMENT
MEDIUMlite/examples/object_detection/raspberry_pi/detect.py56 # Initialize the object detection modelCOMMENT
MEDIUMlite/examples/object_detection/raspberry_pi/detect.py79 # Create a TensorImage object from the RGB image.COMMENT
MEDIUM…examples/audio_classification/raspberry_pi/classify.py45 # Initialize the audio classification model.COMMENT
MEDIUM…examples/sound_classification/raspberry_pi/classify.py45 # Initialize the audio classification model.COMMENT
MEDIUM…ification/android_play_services/.google/packaging.yaml3# This file is used by Google as part of our samples packaging process.COMMENT
MEDIUM…examples/image_classification/raspberry_pi/classify.py49 # Initialize the image classification modelCOMMENT
MEDIUM…_examples/lite/model_maker/demo/recommendation_demo.py125 # Create a model and train.COMMENT
MEDIUM…examples/lite/model_maker/core/data_util/dataloader.py37 """This class provides generic utilities for loading customized domain data that will be used later in model retraininSTRING
MEDIUM…del_maker/core/data_util/object_detector_dataloader.py365 """This function isn't implemented for the object detection task."""STRING
MEDIUM…es/lite/model_maker/core/task/audio_classifier_test.py146 # Create a new dataset without preprocessing since preprocessing has beenCOMMENT
MEDIUM…text_classifier/metadata_writer_for_text_classifier.py99 # Create the vocab file.COMMENT
MEDIUM…text_classifier/metadata_writer_for_text_classifier.py106 # Create the RegexTokenizer.COMMENT
MEDIUM…del_maker/third_party/efficientdet/keras/util_keras.py242### The following code breaks COCO training.COMMENT
MEDIUM…odel_maker/third_party/efficientdet/aug/autoaugment.py396 # Create the new bbox tensor by converting pixel integer values to floats.COMMENT
MEDIUM…odel_maker/third_party/efficientdet/aug/autoaugment.py545 # Create a mask that will be used to zero out a part of the original image.COMMENT
MEDIUM…_maker/third_party/efficientdet/visualize/vis_utils.py832 # Create a display string (and color) for every box location, group any boxesCOMMENT
MEDIUMtensorflow_examples/models/nmt_with_attention/utils.py160 # Create a tf.data datasetCOMMENT
Deep Nesting47 hits · 47 pts
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LOWlite/examples/recommendation/ml/model/utils.py51CODE
LOWlite/examples/reinforcement_learning/ml/common.py87CODE
LOW…amples/pose_estimation/raspberry_pi/pose_estimation.py28CODE
LOWlite/examples/speech_commands/ml/model.py121CODE
LOWlite/examples/speech_commands/ml/generator.py270CODE
LOW…les/lite/model_maker/demo/audio_classification_demo.py173CODE
LOW…ite/model_maker/core/data_util/text_dataloader_test.py109CODE
LOW…_examples/lite/model_maker/core/data_util/data_util.py24CODE
LOW…odel_maker/core/data_util/image_searcher_dataloader.py94CODE
LOW…del_maker/core/data_util/object_detector_dataloader.py63CODE
LOW…xamples/lite/model_maker/core/utils/scann_converter.py59CODE
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LOW…sorflow_examples/lite/model_maker/core/task/configs.py29CODE
LOW…sorflow_examples/lite/model_maker/core/task/configs.py32CODE
LOW…flow_examples/lite/model_maker/core/task/hub_loader.py49CODE
LOW…examples/lite/model_maker/core/task/text_classifier.py122CODE
LOW…les/lite/model_maker/core/task/model_spec/text_spec.py426CODE
LOW…les/lite/model_maker/core/task/model_spec/text_spec.py847CODE
LOW…ow_examples/lite/model_maker/core/api/api_util_test.py154CODE
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LOW…el_maker/third_party/efficientdet/efficientdet_arch.py488CODE
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LOW…les/lite/model_maker/third_party/efficientdet/utils.py454CODE
LOW…model_maker/third_party/efficientdet/hparams_config.py62CODE
LOW…model_maker/third_party/efficientdet/hparams_config.py91CODE
LOW…model_maker/third_party/efficientdet/hparams_config.py122CODE
LOW…ite/model_maker/third_party/efficientdet/dataloader.py253CODE
LOW…te/model_maker/third_party/efficientdet/coco_metric.py177CODE
LOW…te/model_maker/third_party/efficientdet/coco_metric.py239CODE
LOW…ples/lite/model_maker/third_party/efficientdet/main.py123CODE
LOW…odel_maker/third_party/efficientdet/keras/infer_lib.py337CODE
LOW…odel_maker/third_party/efficientdet/keras/inspector.py69CODE
LOW…r/third_party/efficientdet/keras/efficientdet_keras.py78CODE
LOW…r/third_party/efficientdet/keras/efficientdet_keras.py132CODE
LOW…r/third_party/efficientdet/keras/efficientdet_keras.py731CODE
LOW…del_maker/third_party/efficientdet/keras/util_keras.py128CODE
LOW…odel_maker/third_party/efficientdet/keras/train_lib.py729CODE
LOW…te/model_maker/third_party/efficientdet/keras/train.py152CODE
LOW…ker/third_party/efficientdet/dataset/label_map_util.py68CODE
LOW…ird_party/efficientdet/dataset/create_coco_tfrecord.py66CODE
LOW…_maker/third_party/efficientdet/visualize/vis_utils.py764CODE
LOW…hird_party/efficientdet/backbone/efficientnet_model.py640CODE
LOW…hird_party/efficientdet/backbone/efficientnet_model.py711CODE
AI Structural Patterns32 hits · 32 pts
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LOW…recommendation/ml/data/example_generation_movielens.py392CODE
LOWlite/examples/speech_commands/ml/utils.py23CODE
LOWtensorflow_examples/lite/model_maker/core/test_util.py140CODE
LOW…odel_maker/core/data_util/recommendation_dataloader.py147CODE
LOW…odel_maker/core/data_util/recommendation_dataloader.py226CODE
LOW…xamples/lite/model_maker/core/task/image_classifier.py255CODE
LOW…sorflow_examples/lite/model_maker/core/task/configs.py62CODE
LOW…les/lite/model_maker/core/task/classification_model.py143CODE
LOW…es/lite/model_maker/core/task/image_classifier_test.py57CODE
LOW…_examples/lite/model_maker/core/task/recommendation.py211CODE
LOW…_examples/lite/model_maker/core/task/recommendation.py214CODE
LOW…les/lite/model_maker/core/task/model_spec/text_spec.py66CODE
LOW…les/lite/model_maker/core/task/model_spec/text_spec.py333CODE
LOW…les/lite/model_maker/core/task/model_spec/text_spec.py459CODE
LOW…les/lite/model_maker/core/task/model_spec/text_spec.py758CODE
LOW…del_maker/core/task/model_spec/object_detector_spec.py115CODE
LOW…recommendation/ml/data/example_generation_movielens.py392CODE
LOW…/model_maker/third_party/efficientdet/model_inspect.py78CODE
LOW…les/lite/model_maker/third_party/efficientdet/utils.py391CODE
LOW…r/third_party/efficientdet/keras/efficientdet_keras.py237CODE
LOW…r/third_party/efficientdet/keras/efficientdet_keras.py339CODE
LOW…r/third_party/efficientdet/keras/efficientdet_keras.py487CODE
LOW…odel_maker/third_party/efficientdet/aug/autoaugment.py1091CODE
LOW…_maker/third_party/efficientdet/visualize/vis_utils.py403CODE
LOW…_maker/third_party/efficientdet/visualize/vis_utils.py764CODE
LOW…maker/third_party/efficientdet/backbone/autoaugment.py315CODE
LOWtensorflow_examples/models/densenet/densenet.py165CODE
LOWtensorflow_examples/models/densenet/densenet.py197CODE
LOWtensorflow_examples/models/densenet/densenet.py269CODE
LOW…nsorflow_examples/models/densenet/distributed_train.py184CODE
LOWtensorflow_examples/models/densenet/train.py160CODE
LOW…g/imagenet_preprocessing_ineffecient_input_pipeline.py295CODE
Excessive Try-Catch Wrapping6 hits · 10 pts
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MEDIUM…/ios/ImageClassifierBuilder/CameraViewController.swift128 print("Error Unable to initialize camera: \(error.localizedDescription)")CODE
MEDIUM…Classification/ModelDataHandler/ModelDataHandler.swift230 print("Error: out of memory")CODE
MEDIUM…/model_maker/third_party/efficientdet/model_inspect.py225 print('Error opening input video: {}'.format(video_path))CODE
MEDIUM…odel_maker/third_party/efficientdet/keras/inspector.py153 print('Error opening input video: {}'.format(FLAGS.input_video))CODE
LOW…/third_party/efficientdet/dataset/inspect_tfrecords.py90 except Exception: # pylint: disable=broad-exceptCODE
LOW…/third_party/efficientdet/dataset/inspect_tfrecords.py125 except Exception as e: # pylint: disable=broad-exceptCODE
Hallucination Indicators1 hit · 10 pts
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CRITICAL…les/lite/model_maker/third_party/efficientdet/utils.py648 tf.compat.v1.keras.layers.enable_v2_dtype_behavior()CODE
AI Slop Vocabulary4 hits · 9 pts
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MEDIUM…te/examples/pose_estimation/raspberry_pi/visualizer.py107 # Thunder 3 times to leverage the cropping logic and improve accuracy.COMMENT
MEDIUM…model_maker/core/data_util/searcher_dataloader_test.py70 # from raw input leverage embedder later.COMMENT
LOW…del_maker/third_party/efficientdet/keras/label_util.py138 # case 1: if it is None or dict, just return it.COMMENT
LOW…odel_maker/third_party/efficientdet/aug/autoaugment.py1587 # If no bounding boxes were specified, then just return the images.COMMENT
Redundant / Tautological Comments5 hits · 8 pts
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LOWlite/tools/build_ios_app.sh38 # Check if this directory appears in the skipped builds list.COMMENT
LOWlite/tools/build_android_app.sh31 # Check if this directory appears in the skipped builds list.COMMENT
LOWlite/tools/build_android_app.sh42 # Check if the directory contains a gradle wrapper.COMMENT
LOW…o_classification/raspberry_pi/video_classifier_test.py68 # Check if TOP-1 result match.COMMENT
LOW…amples/pose_estimation/raspberry_pi/pose_estimation.py110 # Check if all keypoints are detected before running the classifier.COMMENT
Structural Annotation Overuse5 hits · 8 pts
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LOW…es/lite/model_maker/third_party/efficientdet/README.md182 # Step 1: export saved model.COMMENT
LOW…es/lite/model_maker/third_party/efficientdet/README.md188 # Step 2: do inference with saved model.COMMENT
LOW…es/lite/model_maker/third_party/efficientdet/README.md199 # Step 2: do inference with frozen graph.COMMENT
LOW…e/model_maker/third_party/efficientdet/keras/README.md154 # Step 2: do inference with frozen graph.COMMENT
LOW…e/model_maker/third_party/efficientdet/keras/README.md163 # Step 2: do inference with frozen graph.COMMENT
Modern AI Meta-Vocabulary2 hits · 4 pts
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MEDIUMlite/examples/classification_by_retrieval/README.md7as [one image per class](#model-accuracy-comparison-with-few-shot-learning), orCODE
MEDIUMlite/examples/generative_ai/android/README.md129As noted in the original [OpenAI GPT-2 announcement](https://openai.com/research/better-language-models), there are [notCODE
Slop Phrases2 hits · 3 pts
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LOWtensorflow_examples/models/densenet/densenet.py134 # don't forget to set use_bias=False when using batchnormCOMMENT
LOWtensorflow_examples/models/densenet/densenet.py134 # don't forget to set use_bias=False when using batchnormCOMMENT
Modern Structural Boilerplate1 hit · 1 pts
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LOW…amples/pose_estimation/raspberry_pi/tracker/tracker.py144 def _update_tracks(self) -> None:CODE
Overly Generic Function Names1 hit · 1 pts
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LOW…er/third_party/efficientdet/backbone/train_backbone.py87 def process_data(image, label):CODE