ImageEmbedder¶
- class flash.image.embedding.model.ImageEmbedder(training_strategy, head, pretraining_transform, backbone='resnet', pretrained=False, optimizer='Adam', lr_scheduler=None, learning_rate=None, backbone_kwargs=None, training_strategy_kwargs=None, pretraining_transform_kwargs=None)[source]¶
The
ImageEmbedderis aTaskfor obtaining feature vectors (embeddings) from images. For more details, see Image Embedder.- Parameters
training_strategy¶ (
str) – Training strategy from VISSL, select between ‘simclr’, ‘swav’, ‘dino’, ‘moco’, or ‘barlow_twins’.head¶ (
str) – projection head used for task, select between ‘simclr_head’, ‘swav_head’, ‘dino_head’, ‘moco_head’, or ‘barlow_twins_head’.pretraining_transform¶ (
str) – transform applied to input image for pre-training SSL model. Select between ‘simclr_transform’, ‘swav_transform’, ‘dino_transform’, ‘moco_transform’, or ‘barlow_twins_transform’.pretrained¶ (
bool) – Use a pretrained backbone, defaults toFalse.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]) – Learning rate to use for training, defaults to1e-3.backbone_kwargs¶ (
Optional[Dict[str,Any]]) – arguments to be passed to VISSL backbones, i.e.vision_transformerandresnet.training_strategy_kwargs¶ (
Optional[Dict[str,Any]]) – arguments passed to VISSL loss function, projection head and training hooks.pretraining_transform_kwargs¶ (
Optional[Dict[str,Any]]) – arguments passed to VISSL transforms.
- classmethod available_finetuning_strategies(cls)¶
Returns a list containing the keys of the available Finetuning Strategies.
- classmethod available_lr_schedulers(cls)¶
Returns a list containing the keys of the available LR schedulers.