otx.algo.classification.hlabel_models#

hlabel classification models package.

Classes

EfficientNetHLabelCls(label_info, ...)

EfficientNet Model for hierarchical label classification task.

MobileNetV3HLabelCls(label_info, ...)

MobileNetV3 Model for hierarchical label classification task.

TimmModelHLabelCls(label_info, ...)

Timm Model for hierarchical label classification task.

TVModelHLabelCls(label_info, ...)

TVModelForHLabelCls class represents a Torchvision model for hierarchical label classification.

VisionTransformerHLabelCls(label_info, ...)

VisionTransformerForHLabelCls is a model designed for hierarchical label classification using ViT architecture.
class otx.algo.classification.hlabel_models.EfficientNetHLabelCls(label_info: HLabelInfo, data_input_params: DataInputParams, model_name: str = 'efficientnet_b0', optimizer: OptimizerCallable = <function _default_optimizer_callable>, scheduler: LRSchedulerCallable | LRSchedulerListCallable = <function _default_scheduler_callable>, metric: MetricCallable = <function _mixed_hlabel_accuracy>, torch_compile: bool = False)[source]#

Bases: OTXHlabelClsModel

EfficientNet Model for hierarchical label classification task.

Initialize the base model with the given parameters.

Parameters:

  • label_info (LabelInfoTypes) – Information about the labels used in the model.

  • data_input_params (DataInputParams) – Parameters of the input data such as input size, mean, and std.

  • model_name (str, optional) – Name of the model. Defaults to “OTXModel”.

  • optimizer (OptimizerCallable, optional) – Callable for the optimizer. Defaults to DefaultOptimizerCallable.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable) – Callable for the learning rate scheduler. Defaults to DefaultSchedulerCallable.

  • metric (MetricCallable, optional) – Callable for the metric. Defaults to NullMetricCallable.

  • torch_compile (bool, optional) – Flag to indicate if torch.compile should be used. Defaults to False.

  • tile_config (TileConfig, optional) – Configuration for tiling. Defaults to TileConfig(enable_tiler=False).

Returns:

None

load_from_otx_v1_ckpt(state_dict: dict, add_prefix: str = 'model.') → dict[source]#

Load the previous OTX ckpt according to OTX2.0.

class otx.algo.classification.hlabel_models.MobileNetV3HLabelCls(label_info: HLabelInfo, data_input_params: DataInputParams, model_name: str = 'mobilenetv3_large', optimizer: OptimizerCallable = <function _default_optimizer_callable>, scheduler: LRSchedulerCallable | LRSchedulerListCallable = <function _default_scheduler_callable>, metric: MetricCallable = <function _mixed_hlabel_accuracy>, torch_compile: bool = False)[source]#

Bases: OTXHlabelClsModel

MobileNetV3 Model for hierarchical label classification task.

Initialize the base model with the given parameters.

Parameters:

  • label_info (LabelInfoTypes) – Information about the labels used in the model.

  • data_input_params (DataInputParams) – Parameters of the input data such as input size, mean, and std.

  • model_name (str, optional) – Name of the model. Defaults to “OTXModel”.

  • optimizer (OptimizerCallable, optional) – Callable for the optimizer. Defaults to DefaultOptimizerCallable.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable) – Callable for the learning rate scheduler. Defaults to DefaultSchedulerCallable.

  • metric (MetricCallable, optional) – Callable for the metric. Defaults to NullMetricCallable.

  • torch_compile (bool, optional) – Flag to indicate if torch.compile should be used. Defaults to False.

  • tile_config (TileConfig, optional) – Configuration for tiling. Defaults to TileConfig(enable_tiler=False).

Returns:

None

load_from_otx_v1_ckpt(state_dict: dict, add_prefix: str = 'model.') → dict[source]#

Load the previous OTX ckpt according to OTX2.0.

class otx.algo.classification.hlabel_models.TVModelHLabelCls(label_info: HLabelInfo, data_input_params: DataInputParams, model_name: str = 'efficientnet_v2_s', optimizer: OptimizerCallable = <function _default_optimizer_callable>, scheduler: LRSchedulerCallable | LRSchedulerListCallable = <function _default_scheduler_callable>, metric: MetricCallable = <function _mixed_hlabel_accuracy>, torch_compile: bool = False)[source]#

Bases: OTXHlabelClsModel

TVModelForHLabelCls class represents a Torchvision model for hierarchical label classification.

Parameters:

  • label_info (HLabelInfo) – Information about the hierarchical labels.

  • backbone (TVModelType) – The type of Torchvision backbone model.

  • pretrained (bool, optional) – Whether to use pretrained weights. Defaults to True.

  • optimizer (OptimizerCallable, optional) – The optimizer callable. Defaults to DefaultOptimizerCallable.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable, optional) – The learning rate scheduler callable. Defaults to DefaultSchedulerCallable.

  • metric (MetricCallable, optional) – The metric callable. Defaults to HLabelClsMetricCallble.

  • torch_compile (bool, optional) – Whether to compile the model using TorchScript. Defaults to False.

  • input_size (tuple[int, int], optional) – The input size of the images. Defaults to (224, 224).

Initialize the base model with the given parameters.

Parameters:

  • label_info (LabelInfoTypes) – Information about the labels used in the model.

  • data_input_params (DataInputParams) – Parameters of the input data such as input size, mean, and std.

  • model_name (str, optional) – Name of the model. Defaults to “OTXModel”.

  • optimizer (OptimizerCallable, optional) – Callable for the optimizer. Defaults to DefaultOptimizerCallable.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable) – Callable for the learning rate scheduler. Defaults to DefaultSchedulerCallable.

  • metric (MetricCallable, optional) – Callable for the metric. Defaults to NullMetricCallable.

  • torch_compile (bool, optional) – Flag to indicate if torch.compile should be used. Defaults to False.

  • tile_config (TileConfig, optional) – Configuration for tiling. Defaults to TileConfig(enable_tiler=False).

Returns:

None

class otx.algo.classification.hlabel_models.TimmModelHLabelCls(label_info: HLabelInfo, data_input_params: DataInputParams, model_name: str = 'tf_efficientnetv2_s.in21k', optimizer: OptimizerCallable = <function _default_optimizer_callable>, scheduler: LRSchedulerCallable | LRSchedulerListCallable = <function _default_scheduler_callable>, metric: MetricCallable = <function _mixed_hlabel_accuracy>, torch_compile: bool = False)[source]#

Bases: OTXHlabelClsModel

Timm Model for hierarchical label classification task.

Parameters:

  • label_info (HLabelInfo) – The label information for the classification task.

  • model_name (str) – The name of the model. You can find available models at timm.list_models() or timm.list_pretrained().

  • input_size (tuple[int, int], optional) – Model input size in the order of height and width. Defaults to (224, 224).

  • pretrained (bool, optional) – Whether to load pretrained weights. Defaults to True.

  • optimizer (OptimizerCallable, optional) – The optimizer callable for training the model.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable, optional) – The learning rate scheduler callable.

  • metric (MetricCallable, optional) – The metric callable for evaluating the model. Defaults to HLabelClsMetricCallable.

  • torch_compile (bool, optional) – Whether to compile the model using TorchScript. Defaults to False.

Initialize the base model with the given parameters.

Parameters:

  • label_info (LabelInfoTypes) – Information about the labels used in the model.

  • data_input_params (DataInputParams) – Parameters of the input data such as input size, mean, and std.

  • model_name (str, optional) – Name of the model. Defaults to “OTXModel”.

  • optimizer (OptimizerCallable, optional) – Callable for the optimizer. Defaults to DefaultOptimizerCallable.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable) – Callable for the learning rate scheduler. Defaults to DefaultSchedulerCallable.

  • metric (MetricCallable, optional) – Callable for the metric. Defaults to NullMetricCallable.

  • torch_compile (bool, optional) – Flag to indicate if torch.compile should be used. Defaults to False.

  • tile_config (TileConfig, optional) – Configuration for tiling. Defaults to TileConfig(enable_tiler=False).

Returns:

None

load_from_otx_v1_ckpt(state_dict: dict, add_prefix: str = 'model.') → dict[source]#

Load the previous OTX ckpt according to OTX2.0.

class otx.algo.classification.hlabel_models.VisionTransformerHLabelCls(label_info: HLabelInfo, data_input_params: DataInputParams, model_name: str = 'vit-tiny', lora: bool = False, optimizer: OptimizerCallable = <function _default_optimizer_callable>, scheduler: LRSchedulerCallable | LRSchedulerListCallable = <function _default_scheduler_callable>, metric: MetricCallable = <function _mixed_hlabel_accuracy>, torch_compile: bool = False)[source]#

Bases: ForwardExplainMixInForViT, OTXHlabelClsModel

VisionTransformerForHLabelCls is a model designed for hierarchical label classification using ViT architecture.

Parameters:

  • label_info (HLabelInfo) – Information about the hierarchical labels.

  • lora (bool) – Whether to use LoRA (Low-Rank Adaptation) for the model.

  • model_name (str) – Name of the Vision Transformer model to use.

  • data_input_params (DataInputParams) – Parameters for data input.

  • optimizer (OptimizerCallable) – Callable for the optimizer.

  • scheduler (LRSchedulerCallable | LRSchedulerListCallable) – Callable for the learning rate scheduler.

  • metric (MetricCallable) – Callable for the metric.

  • torch_compile (bool) – Whether to use torch.compile for the model.

load_from_otx_v1_ckpt(state_dict: dict, add_prefix: str = 'model.') → dict[source]#

Load the previous OTX ckpt according to OTX2.0.