Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
This report summarizes scanner evidence for qubvel-org/segmentation_models.pytorch, a Python project with 11,757 GitHub stars. SynthScan examined 19,279 lines of code across 148 source files, recording 253 pattern matches distributed across 12 syntactic categories. The legacy adjusted pattern signal is 20.4 (Elevated pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 124 finding(s).
Are imports declared and package/API names verified? 61 finding(s).
Are wrappers, guards, and repeated structures necessary? 162 finding(s).
Which changes need focused human review? 253 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 253 distinct pattern matches across 12 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 63 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 65 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 72 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 74 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 88 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 90 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 99 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 101 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 374 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 376 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 410 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 412 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 421 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 423 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 451 | # ---------------------------- | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 453 | # ---------------------------- | COMMENT |
| MEDIUM | segmentation_models_pytorch/encoders/mix_transformer.py | 1 | # --------------------------------------------------------------- | COMMENT |
| MEDIUM | segmentation_models_pytorch/encoders/mix_transformer.py | 10 | # --------------------------------------------------------------- | COMMENT |
| MEDIUM | segmentation_models_pytorch/encoders/mix_transformer.py | 521 | # --------------------------------------------------------------- | COMMENT |
| MEDIUM | segmentation_models_pytorch/encoders/mix_transformer.py | 523 | # --------------------------------------------------------------- | COMMENT |
| MEDIUM | segmentation_models_pytorch/encoders/_xception.py | 188 | # #----------------------------- | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | misc/generate_table_timm.py | 5 | def check_features_and_reduction(name): | CODE |
| LOW | misc/generate_table_timm.py | 20 | def valid_vit_encoder_for_dpt(name): | CODE |
| LOW | tests/conftest.py | 7 | def pytest_collection_modifyitems(config, items): | CODE |
| LOW | tests/test_losses.py | 16 | def test_focal_loss_from_logits_false_multiclass(): | CODE |
| LOW | tests/test_losses.py | 42 | def test_focal_loss_with_logits(): | CODE |
| LOW | tests/test_losses.py | 52 | def test_softmax_focal_loss_with_logits(): | CODE |
| LOW | tests/test_losses.py | 241 | def test_multiclass_jaccard_loss(): | CODE |
| LOW | tests/test_losses.py | 268 | def test_multilabel_jaccard_loss(): | CODE |
| LOW | tests/test_losses.py | 365 | def test_class_weights_uniform_equivalent_to_no_weights_multiclass(): | CODE |
| LOW | tests/test_losses.py | 383 | def test_class_weights_uniform_equivalent_to_no_weights_multilabel(): | CODE |
| LOW | tests/test_losses.py | 401 | def test_class_weights_nonuniform_changes_loss_multiclass(): | CODE |
| LOW | tests/test_losses.py | 418 | def test_class_weights_scale_invariant_multiclass(): | CODE |
| LOW | tests/test_losses.py | 438 | def test_class_weights_binary_mode_raises(): | CODE |
| LOW | tests/test_losses.py | 446 | def test_focal_class_weights_uniform_equivalent_to_no_weights(): | CODE |
| LOW | tests/test_losses.py | 461 | def test_focal_class_weights_scale_invariant(): | CODE |
| LOW | tests/test_preprocessing.py | 39 | def test_preprocessing_params(): | CODE |
| LOW⚡ | tests/utils.py | 31 | def requires_timm_greater_or_equal(version: str): | CODE |
| LOW⚡ | tests/utils.py | 40 | def requires_torch_greater_or_equal(version: str): | CODE |
| LOW⚡ | tests/utils.py | 49 | def check_run_test_on_diff_or_main(filepath_patterns: List[str]): | CODE |
| LOW | tests/utils.py | 72 | def check_two_models_strictly_equal( | CODE |
| LOW | tests/test_base.py | 8 | def test_from_pretrained_with_mismatched_keys(): | CODE |
| LOW | tests/models/test_linknet.py | 12 | def test_timm_transformer_style_encoder(self): | CODE |
| LOW | tests/models/test_unetplusplus.py | 11 | def test_timm_transformer_style_encoder(self): | CODE |
| LOW | tests/models/base.py | 94 | def test_in_channels_and_depth_and_out_classes( | CODE |
| LOW | tests/models/base.py | 174 | def test_save_load_with_hub_mixin(self): | CODE |
| LOW | tests/models/base.py | 207 | def test_preserve_forward_output(self): | CODE |
| LOW | tests/encoders/test_common.py | 13 | def test_load_encoder_from_hub(encoder_name_and_weights): | CODE |
| LOW | tests/encoders/test_batchnorm_deprecation.py | 11 | def test_seg_models_before_after_use_norm(model_name, decoder_option): | CODE |
| LOW | tests/encoders/test_batchnorm_deprecation.py | 35 | def test_pspnet_before_after_use_norm(decoder_option): | CODE |
| LOW⚡ | tests/encoders/test_timm_universal.py | 28 | def test_string_weights_emits_warning(self): | CODE |
| LOW⚡ | tests/encoders/test_timm_universal.py | 32 | def test_true_weights_no_warning(self): | CODE |
| LOW⚡ | tests/encoders/test_timm_universal.py | 37 | def test_none_weights_no_warning(self): | CODE |
| LOW | tests/encoders/base.py | 47 | def get_features_output_strides(self, sample, features): | CODE |
| LOW⚡ | tests/base/test_freeze_encoder.py | 5 | def test_freeze_and_unfreeze_encoder(): | CODE |
| LOW⚡ | tests/base/test_freeze_encoder.py | 8 | def assert_encoder_params_trainable(expected: bool): | CODE |
| LOW⚡ | tests/base/test_freeze_encoder.py | 11 | def assert_norm_layers_training(expected: bool): | CODE |
| LOW | tests/base/test_freeze_encoder.py | 50 | def test_freeze_encoder_stops_running_stats(): | CODE |
| LOW | examples/binary_segmentation_buildings.py | 247 | def train_and_evaluate_one_epoch( | CODE |
| LOW | scripts/models-conversions/upernet-hf-to-smp.py | 120 | def convert_old_keys_to_new_keys(state_dict_keys: dict, keys_mapping: dict): | CODE |
| LOW | …odels-conversions/segformer-original-decoder-to-smp.py | 13 | def convert_state_dict_to_smp(state_dict: dict): | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 357 | def _positive_predictive_value(tp, fp, fn, tn): | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 361 | def _negative_predictive_value(tp, fp, fn, tn): | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 381 | def _positive_likelihood_ratio(tp, fp, fn, tn): | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 385 | def _negative_likelihood_ratio(tp, fp, fn, tn): | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 546 | def positive_predictive_value( | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 568 | def negative_predictive_value( | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 678 | def positive_likelihood_ratio( | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 700 | def negative_likelihood_ratio( | CODE |
| LOW | segmentation_models_pytorch/losses/_functional.py | 100 | def softmax_focal_loss_with_logits( | CODE |
| LOW | segmentation_models_pytorch/encoders/timm_vit.py | 10 | def sample_block_indices_uniformly(n: int, total_num_blocks: int) -> list[int]: | CODE |
| LOW | segmentation_models_pytorch/encoders/timm_vit.py | 35 | def preprocess_output_indices( | CODE |
| LOW | segmentation_models_pytorch/encoders/timm_vit.py | 149 | def _forward_with_prefix_tokens( | CODE |
| LOW | segmentation_models_pytorch/encoders/timm_vit.py | 164 | def _forward_without_prefix_tokens(self, x: torch.Tensor) -> list[torch.Tensor]: | CODE |
| LOW | segmentation_models_pytorch/encoders/__init__.py | 56 | def is_equivalent_to_timm_universal(name): | CODE |
| LOW | …mentation_models_pytorch/encoders/timm_efficientnet.py | 57 | def gen_efficientnet_lite_kwargs( | CODE |
| LOW | segmentation_models_pytorch/encoders/timm_universal.py | 232 | def _merge_kwargs_no_duplicates(a: dict[str, Any], b: dict[str, Any]) -> dict[str, Any]: | CODE |
| LOW | segmentation_models_pytorch/encoders/_efficientnet.py | 436 | def get_width_and_height_from_size(x): | CODE |
| LOW | segmentation_models_pytorch/encoders/_efficientnet.py | 453 | def calculate_output_image_size(input_image_size, stride): | CODE |
| LOW | segmentation_models_pytorch/encoders/_utils.py | 46 | def replace_strides_with_dilation(module, dilation_rate): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | segmentation_models_pytorch/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/__init__.py | 3 | CODE | |
| LOW | segmentation_models_pytorch/__init__.py | 4 | CODE | |
| LOW | segmentation_models_pytorch/__init__.py | 5 | CODE | |
| LOW | segmentation_models_pytorch/__init__.py | 18 | CODE | |
| LOW | segmentation_models_pytorch/__init__.py | 20 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 2 | CODE | |
| LOW | segmentation_models_pytorch/losses/lovasz.py | 6 | CODE | |
| LOW | segmentation_models_pytorch/losses/lovasz.py | 6 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 3 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 4 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 5 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 6 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 7 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 8 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 9 | CODE | |
| LOW | segmentation_models_pytorch/losses/__init__.py | 10 | CODE | |
| LOW | segmentation_models_pytorch/datasets/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/datasets/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/utils/__init__.py | 3 | CODE | |
| LOW | segmentation_models_pytorch/utils/__init__.py | 4 | CODE | |
| LOW | segmentation_models_pytorch/utils/__init__.py | 5 | CODE | |
| LOW | segmentation_models_pytorch/encoders/__init__.py | 27 | CODE | |
| LOW | …gmentation_models_pytorch/decoders/linknet/__init__.py | 1 | CODE | |
| LOW | …ation_models_pytorch/decoders/unetplusplus/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/decoders/pspnet/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/decoders/dpt/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/decoders/manet/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/decoders/unet/__init__.py | 1 | CODE | |
| LOW | …entation_models_pytorch/decoders/segformer/__init__.py | 1 | CODE | |
| LOW | …gmentation_models_pytorch/decoders/upernet/__init__.py | 1 | CODE | |
| LOW | …entation_models_pytorch/decoders/deeplabv3/__init__.py | 1 | CODE | |
| LOW | …entation_models_pytorch/decoders/deeplabv3/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/decoders/fpn/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/decoders/pan/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/base/__init__.py | 1 | CODE | |
| LOW | segmentation_models_pytorch/base/__init__.py | 3 | CODE | |
| LOW | segmentation_models_pytorch/base/__init__.py | 3 | CODE | |
| LOW | segmentation_models_pytorch/base/__init__.py | 5 | CODE | |
| 1 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | segmentation_models_pytorch/encoders/vgg.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | segmentation_models_pytorch/encoders/senet.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | segmentation_models_pytorch/encoders/densenet.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | segmentation_models_pytorch/encoders/inceptionv4.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | segmentation_models_pytorch/encoders/efficientnet.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | segmentation_models_pytorch/encoders/mobilenet.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | segmentation_models_pytorch/encoders/resnet.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | …mentation_models_pytorch/encoders/inceptionresnetv2.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| HIGH | segmentation_models_pytorch/encoders/dpn.py | 0 | each encoder should have following attributes and methods and be inherited from `_base.encodermixin` attributes: _out_ch | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | segmentation_models_pytorch/losses/focal.py | 13 | CODE | |
| LOW | segmentation_models_pytorch/losses/tversky.py | 37 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_dpn.py | 164 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_senet.py | 56 | CODE | |
| LOW | segmentation_models_pytorch/encoders/mix_transformer.py | 165 | CODE | |
| LOW | segmentation_models_pytorch/encoders/mix_transformer.py | 272 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 42 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 65 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 78 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 108 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 129 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 162 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 188 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_inceptionv4.py | 242 | CODE | |
| LOW | …entation_models_pytorch/encoders/_inceptionresnetv2.py | 59 | CODE | |
| LOW | …entation_models_pytorch/encoders/_inceptionresnetv2.py | 92 | CODE | |
| LOW | …entation_models_pytorch/encoders/_inceptionresnetv2.py | 114 | CODE | |
| LOW | …entation_models_pytorch/encoders/_inceptionresnetv2.py | 141 | CODE | |
| LOW | …entation_models_pytorch/encoders/_inceptionresnetv2.py | 172 | CODE | |
| LOW | segmentation_models_pytorch/encoders/mobileone.py | 301 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_efficientnet.py | 433 | CODE | |
| LOW | segmentation_models_pytorch/decoders/linknet/model.py | 74 | CODE | |
| LOW | …entation_models_pytorch/decoders/unetplusplus/model.py | 79 | CODE | |
| LOW | segmentation_models_pytorch/decoders/pspnet/model.py | 75 | CODE | |
| LOW | segmentation_models_pytorch/decoders/dpt/model.py | 81 | CODE | |
| LOW | segmentation_models_pytorch/decoders/manet/model.py | 79 | CODE | |
| LOW | segmentation_models_pytorch/decoders/unet/model.py | 108 | CODE | |
| LOW | …mentation_models_pytorch/decoders/segformer/decoder.py | 73 | CODE | |
| LOW | segmentation_models_pytorch/decoders/segformer/model.py | 52 | CODE | |
| LOW | segmentation_models_pytorch/decoders/upernet/decoder.py | 211 | CODE | |
| LOW | segmentation_models_pytorch/decoders/upernet/model.py | 69 | CODE | |
| LOW | segmentation_models_pytorch/decoders/deeplabv3/model.py | 59 | CODE | |
| LOW | segmentation_models_pytorch/decoders/deeplabv3/model.py | 185 | CODE | |
| LOW | segmentation_models_pytorch/decoders/fpn/model.py | 58 | CODE | |
| LOW | segmentation_models_pytorch/decoders/pan/model.py | 61 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 84 | # Create a directory to store the output masks | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 89 | # Define the hyperparameters | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 100 | # Define a custom dataset class for the CamVid dataset | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 375 | # Define the data directories and create the datasets | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 411 | # Create the dataloaders using the datasets | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 461 | # Define the Adam optimizer | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 464 | # Define the learning rate scheduler | COMMENT |
| MEDIUM⚡ | examples/binary_segmentation_buildings.py | 467 | # Define the loss function | COMMENT |
| MEDIUM | examples/binary_segmentation_buildings.py | 193 | # Define a class for the CamVid model | COMMENT |
| MEDIUM | …odels-conversions/segformer-original-decoder-to-smp.py | 78 | # Create the model | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | segmentation_models_pytorch/__init__.py | 72 | __all__ = [ | CODE |
| LOW | segmentation_models_pytorch/metrics/__init__.py | 23 | __all__ = [ | CODE |
| LOW | segmentation_models_pytorch/metrics/functional.py | 35 | __all__ = [ | CODE |
| LOW | segmentation_models_pytorch/losses/jaccard.py | 9 | __all__ = ["JaccardLoss"] | CODE |
| LOW | segmentation_models_pytorch/losses/focal.py | 9 | __all__ = ["FocalLoss"] | CODE |
| LOW | segmentation_models_pytorch/losses/soft_ce.py | 7 | __all__ = ["SoftCrossEntropyLoss"] | CODE |
| LOW | segmentation_models_pytorch/losses/lovasz.py | 19 | __all__ = ["LovaszLoss"] | CODE |
| LOW | segmentation_models_pytorch/losses/__init__.py | 12 | __all__ = [ | CODE |
| LOW | segmentation_models_pytorch/losses/dice.py | 9 | __all__ = ["DiceLoss"] | CODE |
| LOW | segmentation_models_pytorch/losses/tversky.py | 8 | __all__ = ["TverskyLoss"] | CODE |
| LOW | segmentation_models_pytorch/losses/_functional.py | 10 | __all__ = [ | CODE |
| LOW | segmentation_models_pytorch/losses/soft_bce.py | 7 | __all__ = ["SoftBCEWithLogitsLoss"] | CODE |
| LOW | segmentation_models_pytorch/datasets/__init__.py | 3 | __all__ = ["OxfordPetDataset", "SimpleOxfordPetDataset"] | CODE |
| LOW | segmentation_models_pytorch/utils/__init__.py | 7 | __all__ = ["train", "losses", "metrics"] | CODE |
| LOW | segmentation_models_pytorch/encoders/__init__.py | 31 | __all__ = [ | CODE |
| LOW | segmentation_models_pytorch/encoders/mobileone.py | 15 | __all__ = ["MobileOne", "reparameterize_model"] | CODE |
| LOW | …gmentation_models_pytorch/decoders/linknet/__init__.py | 3 | __all__ = ["Linknet"] | CODE |
| LOW | …ation_models_pytorch/decoders/unetplusplus/__init__.py | 3 | __all__ = ["UnetPlusPlus"] | CODE |
| LOW | segmentation_models_pytorch/decoders/pspnet/__init__.py | 3 | __all__ = ["PSPNet"] | CODE |
| LOW | segmentation_models_pytorch/decoders/dpt/__init__.py | 3 | __all__ = ["DPT"] | CODE |
| LOW | segmentation_models_pytorch/decoders/manet/__init__.py | 3 | __all__ = ["MAnet"] | CODE |
| LOW | segmentation_models_pytorch/decoders/unet/__init__.py | 3 | __all__ = ["Unet"] | CODE |
| LOW | …entation_models_pytorch/decoders/segformer/__init__.py | 3 | __all__ = ["Segformer"] | CODE |
| LOW | …gmentation_models_pytorch/decoders/upernet/__init__.py | 3 | __all__ = ["UPerNet"] | CODE |
| LOW | …mentation_models_pytorch/decoders/deeplabv3/decoder.py | 40 | __all__ = ["DeepLabV3Decoder", "DeepLabV3PlusDecoder"] | CODE |
| LOW | …entation_models_pytorch/decoders/deeplabv3/__init__.py | 3 | __all__ = ["DeepLabV3", "DeepLabV3Plus"] | CODE |
| LOW | segmentation_models_pytorch/decoders/fpn/__init__.py | 3 | __all__ = ["FPN"] | CODE |
| LOW | segmentation_models_pytorch/decoders/pan/__init__.py | 3 | __all__ = ["PAN"] | CODE |
| LOW | segmentation_models_pytorch/base/__init__.py | 7 | __all__ = [ | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | segmentation_models_pytorch/metrics/functional.py | 255 | CODE | |
| LOW | segmentation_models_pytorch/losses/focal.py | 77 | CODE | |
| LOW | segmentation_models_pytorch/encoders/_dpn.py | 279 | CODE | |
| LOW | segmentation_models_pytorch/encoders/mix_transformer.py | 113 | CODE | |
| LOW | segmentation_models_pytorch/encoders/mix_transformer.py | 203 | CODE | |
| LOW | segmentation_models_pytorch/encoders/mix_transformer.py | 250 | CODE | |
| LOW | segmentation_models_pytorch/encoders/mix_transformer.py | 413 | CODE | |
| LOW | segmentation_models_pytorch/decoders/deeplabv3/model.py | 125 | CODE | |
| LOW | segmentation_models_pytorch/base/model.py | 97 | CODE | |
| LOW | segmentation_models_pytorch/base/initialization.py | 4 | CODE | |
| LOW | segmentation_models_pytorch/base/modules.py | 12 | CODE | |
| LOW | segmentation_models_pytorch/base/modules.py | 152 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | misc/generate_table_timm.py | 16 | except Exception: | CODE |
| LOW | misc/generate_table_timm.py | 97 | except Exception: | CODE |
| LOW | misc/generate_table_timm.py | 101 | except Exception: | CODE |
| MEDIUM | misc/generate_table_timm.py | 11 | def has_dilation_support(name): | CODE |
| LOW⚡ | tests/utils.py | 58 | except Exception: | CODE |
| LOW | segmentation_models_pytorch/encoders/timm_universal.py | 98 | except Exception: | CODE |
| LOW | segmentation_models_pytorch/base/utils.py | 8 | except Exception: | CODE |
| LOW | segmentation_models_pytorch/base/utils.py | 13 | except Exception: | CODE |
| MEDIUM | segmentation_models_pytorch/base/utils.py | 5 | def is_torch_compiling(): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | segmentation_models_pytorch/metrics/functional.py | 70 | Compute true positive, false positive, false negative, true negative 'pixels' for each image and each class. Ar | STRING |
| HIGH | segmentation_models_pytorch/encoders/_efficientnet.py | 728 | Get a block through a string notation of arguments. Args: block_string (str): A string notation of | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/conftest.py | 11 | # Check if the test has no marks | COMMENT |
| LOW | segmentation_models_pytorch/encoders/timm_universal.py | 101 | # Check if model output is in channel-last format (NHWC) | COMMENT |
| LOW | segmentation_models_pytorch/encoders/mobileone.py | 104 | # Check if SE-ReLU is requested | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | docs/conf.py | 1 | # Configuration file for the Sphinx documentation builder. | COMMENT |
| LOW | segmentation_models_pytorch/losses/constants.py | 1 | #: Loss binary mode suppose you are solving binary segmentation task. | COMMENT |
| LOW | segmentation_models_pytorch/encoders/_efficientnet.py | 341 | ################################################################################ | COMMENT |