DeepTimmImageEmbedder

class lightautoml.image.image.DeepTimmImageEmbedder(device=torch.device, n_jobs=4, random_state=42, model_name='efficientnet_b0.ra_in1k', weights_path=None, batch_size=128, verbose=True)[source]

Bases: TransformerMixin

Timm Transformer for image embeddings.

__init__(device=torch.device, n_jobs=4, random_state=42, model_name='efficientnet_b0.ra_in1k', weights_path=None, batch_size=128, verbose=True)[source]

Pytorch Dataset for TimmModelEmbedder.

Parameters:

device (device) – Torch device.
n_jobs – Number of threads for dataloader.
random_state – Random seed.
model_name – Name of effnet model.
weights_path (Optional[str]) – Path to saved weights.
batch_size (int) – Batch size.
verbose (bool) – Verbose data processing.

fit(data=None)[source]: Train model.

transform(data)

Calculate image embeddings from paths.

Parameters:: data (Sequence[str]) – Sequence of paths.
Return type:: ndarray
Returns:: Array of embeddings.