KeypointDetector

class flash.image.keypoint_detection.model.KeypointDetector(num_keypoints, num_classes=2, backbone='resnet18_fpn', head='keypoint_rcnn', pretrained=True, optimizer='Adam', lr_scheduler=None, learning_rate=None, predict_kwargs=None, **kwargs)

The KeypointDetector is a Task for detecting keypoints in images. For more details, see Keypoint Detection.

Parameters

• num_keypoints (int) – Number of keypoints to detect.

• num_classes (int) – The number of keypoint classes.

• backbone (Optional[str]) – String indicating the backbone CNN architecture to use.

• head (Optional[str]) – String indicating the head module to use on top of the backbone.

• pretrained (bool) – Whether the model should be loaded with it’s pretrained weights.

• optimizer (TypeVar(OPTIMIZER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], None)) – Optimizer to use for training.

• lr_scheduler (Optional[TypeVar(LR_SCHEDULER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], Tuple[str, Dict[str, Any], Dict[str, Any]], None)]) – The LR scheduler to use during training.

• learning_rate (Optional[float]) – The learning rate to use for training.

• predict_kwargs (Optional[Dict]) – dictionary containing parameters that will be used during the prediction phase.

• **kwargs – additional kwargs used for initializing the task

classmethod available_finetuning_strategies(cls)

Returns a list containing the keys of the available Finetuning Strategies.

Return type

List[str]

classmethod available_lr_schedulers(cls)

Returns a list containing the keys of the available LR schedulers.

Return type

List[str]

classmethod available_optimizers(cls)

Returns a list containing the keys of the available Optimizers.

Return type

List[str]

property predict_kwargs: Dict[str, Any]

The kwargs used for the prediction step.

Return type

Dict[str, Any]