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Golden Recipes

leip_recipe_designer.GoldenVolumes 

GoldenVolumes(task: str = 'vision.detection.2d')

Methods:

get_dataframe 

get_dataframe(key='xval_det')

Retrieves the Golden dataframe for the provided volume.

By default, it pulls the xval_det volume and returns the dataframe.

Parameters:

  • key –

    Provide the volume string, for which you want the Golden Dataframe.

Returns:

  • pd.DataFrame: –

    A Pandas DataFrame of recipes, which contains golden recipes, different metrics, model_family, sppr, backbone, etc.

list_volumes_from_zoo 

list_volumes_from_zoo() -> Dict[str, GoldenDataset]

Retrieve available recipe volumes from LEIP Zoo.

Each volume contains recipes that were tested on one more datasets. Volumes named xval_... are cross validated across diverse datasets and are the recommended starting point.

Returns:

  • datasets ( Dict[str, GoldenDataset] ) –

    A collection of datasets, where the key is a name and the value is the dataset itself.

Examples:

from leip_recipe_designer import GoldenVolumes
goldenvolumes = GoldenVolumes()
goldenvolumes.list_volumes_from_zoo()
>>> {'chemistrylab': <leip_recipe_designer.core.utils.golden_recipe_helpers.GoldenDataset>,
>>> 'bdd100k': <leip_recipe_designer.core.utils.golden_recipe_helpers.GoldenDataset>,
>>> 'insects': <leip_recipe_designer.core.utils.golden_recipe_helpers.GoldenDataset>,
>>> 'wheat': <leip_recipe_designer.core.utils.golden_recipe_helpers.GoldenDataset>
>>> 'carsimple': <leip_recipe_designer.core.utils.golden_recipe_helpers.GoldenDataset>,
>>> 'xval_det': <leip_recipe_designer.core.utils.golden_recipe_helpers.GoldenDataset>}

GoldenDataset 

GoldenDataset(resource: str, variant: str, zoo: Zoo, task: str = 'vision.detection.2d')

Methods:

  • anchor_boxes –

    Plots the mean intersection over union (IoU) per each anchor box group (small, medium and large).

  • boxes_info –

    A histogram of the number of bounding boxes for each image in the dataset.

  • class_distribution –

    Plots image and box class distributions.

  • describe_table –

    Displays a table with all the column values in the dataframe and its description.

  • get_golden_df –

    Generates a recipe DataFrame with the metrics calculated on this dataset.

  • get_samples –

    Show a randomly selected subset of data.

  • resolution –

    Plots the resolution of the train and val images.

anchor_boxes 

anchor_boxes() -> ImageViewer

Plots the mean intersection over union (IoU) per each anchor box group (small, medium and large).

The anchor box groupings are calculated using K-means clustering.

Returns:

  • ImageViewer ( ImageViewer ) –

    An ImageViewer containing the anchor box info for train and validation datasets. Can be viewed in Jupyter notebook using IPython.display, or shown in matplotlib by invoking ImageViewer.show().

boxes_info 

boxes_info() -> ImageViewer

A histogram of the number of bounding boxes for each image in the dataset.

Returns:

  • ImageViewer ( ImageViewer ) –

    An ImageViewer containing the histogram for train and validation datasets. Can be viewed in Jupyter notebook using IPython.display, or shown in matplotlib by invoking ImageViewer.show().

class_distribution 

class_distribution() -> ImageViewer

Plots image and box class distributions.

Plot shows how many boxes of each class are present in the training and validation datasets and how many images contain a specific class.

Returns:

  • ImageViewer ( ImageViewer ) –

    An ImageViewer containing the plots for the train and validation datasets. Can be viewed in Jupyter notebook using IPython.display, or shown in matplotlib by invoking ImageViewer.show().

describe_table 

describe_table() -> None

Displays a table with all the column values in the dataframe and its description.

get_golden_df 

get_golden_df(all_columns: bool = False) -> DataFrame

Generates a recipe DataFrame with the metrics calculated on this dataset.

Parameters:

  • all_columns (bool, default: False ) –

    If True, all available metric columns are returned. Otherwise only a subset of more relevant columns are returned.

Returns:

  • pd.DataFrame: –

    A Pandas DataFrame of recipes, which contains golden recipes, different metrics, model_family, sppr, backbone, etc.

get_samples 

get_samples(split: str = 'train', num_samples=16, seed=42) -> ImageViewer

Show a randomly selected subset of data.

Parameters:

  • split (str, default: 'train' ) –

    The dataset subset (either train or val) from which to sample.

  • num_samples –

    The number of samples to return.

  • seed –

    The seed of the random sample.

Returns:

  • ImageViewer ( ImageViewer ) –

    An ImageViewer containing a grid of the selected images. Can be viewed in Jupyter notebook using IPython.display, or shown in matplotlib by invoking ImageViewer.show().

resolution 

resolution() -> ImageViewer

Plots the resolution of the train and val images.

Returns:

  • ImageViewer ( ImageViewer ) –

    An ImageViewer containing the resolution info for train and validation datasets. Can be viewed in Jupyter notebook using IPython.display, or shown in matplotlib by invoking ImageViewer.show().