filter#

Methods to find label issues in image semantic segmentation datasets, where each pixel in an image receives its own class label.

Functions:

find_label_issues(labels, pred_probs, *[, ...])

Returns a boolean mask for the entire dataset, per pixel where True represents an example identified with a label issue and False represents an example of a pixel correctly labeled.
cleanlab.segmentation.filter.find_label_issues(labels, pred_probs, *, batch_size=None, n_jobs=None, verbose=True, **kwargs)[source]#

Returns a boolean mask for the entire dataset, per pixel where True represents an example identified with a label issue and False represents an example of a pixel correctly labeled.

  • N - Number of images in the dataset

  • K - Number of classes in the dataset

  • H - Height of each image

  • W - Width of each image

Tip

If you encounter the error “pred_probs is not defined”, try setting n_jobs=1.

Parameters:

  • labels (ndarray) –

    A discrete array of shape (N,H,W,) of noisy labels for a semantic segmentation dataset, i.e. some labels may be erroneous.

    Format requirements: For a dataset with K classes, each pixel must be labeled using an integer in 0, 1, …, K-1.

    Tip

    If your labels are one hot encoded you can do: labels = np.argmax(labels_one_hot, axis=1) assuming that labels_one_hot is of dimension (N,K,H,W), in order to get properly formatted labels.

  • pred_probs (ndarray) – An array of shape (N,K,H,W,) of model-predicted class probabilities, P(label=k|x) for each pixel x. The prediction for each pixel is an array corresponding to the estimated likelihood that this pixel belongs to each of the K classes. The 2nd dimension of pred_probs must be ordered such that these probabilities correspond to class 0, 1, …, K-1.

  • batch_size (Optional[int]) – Optional size of image mini-batches used for computing the label issues in a streaming fashion (does not affect results, just the runtime and memory requirements). To maximize efficiency, try to use the largest batch_size your memory allows. If not provided, a good default is used.

  • n_jobs (Optional[int]) – Optional number of processes for multiprocessing (default value = 1). Only used on Linux. If n_jobs=None, will use either the number of: physical cores if psutil is installed, or logical cores otherwise.

  • verbose (bool) – Set to False to suppress all print statements.

  • **kwargs –

    • downsample: int, Optional factor to shrink labels and pred_probs by. Default 1 Must be a factor divisible by both the labels and the pred_probs. Larger values of downsample produce faster runtimes but potentially less accurate results due to over-compression. Set to 1 to avoid any downsampling.

Return type:

ndarray

Returns:

label_issues (np.ndarray) – Returns a boolean mask for the entire dataset of length (N,H,W) where True represents a pixel label issue and False represents an example that is correctly labeled.