This module contains generic utilities used for training and inference.

sleap.nn.utils.compute_iou(bbox1: numpy.ndarray, bbox2: numpy.ndarray) → float[source]

Computes the intersection over union for a pair of bounding boxes.

  • bbox1 – Bounding box specified by corner coordinates [y1, x1, y2, x2].

  • bbox2 – Bounding box specified by corner coordinates [y1, x1, y2, x2].


A float scalar calculated as the ratio between the areas of the intersection and the union of the two bounding boxes.

sleap.nn.utils.group_array(X: numpy.ndarray, groups: numpy.ndarray, axis: int = 0) → Dict[numpy.ndarray, numpy.ndarray][source]

Groups an array into a dictionary keyed by a grouping vector.

  • X – Numpy array with length n along the specified axis.

  • groups – Vector of n values denoting the group that each slice of X should be assigned to. This is also referred to as an indicator, indexing, class, or labels vector.

  • axis – Dimension of X to group on. The length of this axis in X must correspond to the length of groups.


A dictionary with keys mapping each unique value in groups to a subset of X.


See this blog post <> for performance comparisons of different approaches.


>>> group_array(np.arange(5), np.array([1, 5, 2, 1, 5]))
{1: array([0, 3]), 5: array([1, 4]), 2: array([2])}