#include <toolkits/object_detection/od_darknet_yolo_model_trainer.hpp>

Public Member Functions
	DarknetYOLOCheckpoint (Config config, const std::string &pretrained_model_path, int random_seed)

	DarknetYOLOCheckpoint (Config config, neural_net::float_array_map weights)

std::unique_ptr< ModelTrainer >	CreateModelTrainer (neural_net::compute_context *context) const override

neural_net::pipeline_spec	ExportToCoreML (const std::string &input_name, const std::string &coordinates_name, const std::string &confidence_name, bool use_nms_layer, float iou_threshold, float confidence_threshold) const override

neural_net::float_array_map	internal_config () const

neural_net::float_array_map	internal_weights () const

Detailed Description

Subclass of Checkpoint that generates DarknetYOLOModelTrainer instances.

Definition at line 127 of file od_darknet_yolo_model_trainer.hpp.

Constructor & Destructor Documentation

◆ DarknetYOLOCheckpoint() [1/2]

turi::object_detection::DarknetYOLOCheckpoint::DarknetYOLOCheckpoint	(	Config	config,
		const std::string &	pretrained_model_path,
		int	random_seed
	)

Initializes a new model, combining the pre-trained warm-start weights with random initialization for the final layers.

◆ DarknetYOLOCheckpoint() [2/2]

turi::object_detection::DarknetYOLOCheckpoint::DarknetYOLOCheckpoint	(	Config	config,
		neural_net::float_array_map	weights
	)

Loads weights saved from a DarknetYOLOModelTrainer.

Member Function Documentation

◆ CreateModelTrainer()

std::unique_ptr<ModelTrainer> turi::object_detection::DarknetYOLOCheckpoint::CreateModelTrainer ( neural_net::compute_context * context ) const

overridevirtual

Loads the checkpoint into an active ModelTrainer instance.

Implements turi::object_detection::Checkpoint.

◆ ExportToCoreML()

neural_net::pipeline_spec turi::object_detection::DarknetYOLOCheckpoint::ExportToCoreML	(	const std::string &	input_name,
		const std::string &	coordinates_name,
		const std::string &	confidence_name,
		bool	use_nms_layer,
		float	iou_threshold,
		float	confidence_threshold
	)		const

overridevirtual

Returns the CoreML spec corresponding to the current model.

The result must be a pipeline that accepts an image input and yields at least two outputs, all with the given names. The outputs must be suitable for passing directly into a NonMaximumSuppression model.

Implements turi::object_detection::Checkpoint.

◆ internal_config()

neural_net::float_array_map turi::object_detection::DarknetYOLOCheckpoint::internal_config ( ) const

Returns the config dictionary used to initialize darknet-yolo backends.

◆ internal_weights()

neural_net::float_array_map turi::object_detection::DarknetYOLOCheckpoint::internal_weights ( ) const

Returns the weights with the keys expected by the backends.

The documentation for this class was generated from the following file:

toolkits/object_detection/od_darknet_yolo_model_trainer.hpp

Public Member Functions