AutoCVWrap
- class lightautoml.transformers.image.AutoCVWrap(model='efficientnet_b0.ra_in1k', weights_path=None, cache_dir='./cache_CV', subs=None, device=torch.device, n_jobs=4, random_state=42, batch_size=128, verbose=True)[source]
Bases:
LAMLTransformer
Calculate image embeddings.
- Parameters:
model – Name of effnet model.
cache_dir (
str
) – Path to cache directory or None.subs (
Optional
[Any
]) – Subsample to fit transformer. IfNone
- full data.device (
device
) – Torch device.n_jobs (
int
) – Number of threads for dataloader.random_state (
int
) – Random state to take subsample and set torch seed.batch_size (
int
) – Batch size for embedding model.verbose (
bool
) – Verbose data processing.
- property features
Features list.
- Returns:
List of features names.
- fit(dataset)[source]
Fit chosen transformer and create feature names.
- Parameters:
dataset (
Union
[NumpyDataset
,PandasDataset
]) – Pandas or Numpy dataset of text features.- Returns:
self.
- transform(dataset)[source]
Transform dataset to image embeddings.
- Parameters:
dataset (
Union
[NumpyDataset
,PandasDataset
]) – Pandas or Numpy dataset of image paths.- Return type:
- Returns:
Numpy dataset with image embeddings.