""" boxes1_corners = convert_to_corners ( boxes1 ) boxes2_corners = convert_to_corners ( boxes2 ) lu = tf. Returns: pairwise IOU matrix with shape `(N, M)`, where the value at ith row jth column holds the IOU between ith box and jth box from boxes1 and boxes2 respectively. boxes2: A tensor with shape `(M, 4)` representing bounding boxes where each box is of the format ``. This will require us toĬalculate the Intersection Over Union (IOU) between all the anchorĭef compute_iou ( boxes1, boxes2 ): """Computes pairwise IOU matrix for given two sets of boxes Arguments: boxes1: A tensor with shape `(N, 4)` representing bounding boxes where each box is of the format ``. To anchor boxes based on the extent of overlapping. concat ( - boxes / 2.0, boxes + boxes / 2.0 ], axis =- 1, )Ĭomputing pairwise Intersection Over Union (IOU)Īs we will see later in the example, we would be assigning ground truth boxes Returns: converted boxes with shape same as that of boxes. concat ( + boxes ) / 2.0, boxes - boxes ], axis =- 1, ) def convert_to_corners ( boxes ): """Changes the box format to corner coordinates Arguments: boxes: A tensor of rank 2 or higher with a shape of `(., num_boxes, 4)` representing bounding boxes where each box is of the format ``. Arguments: boxes: A tensor of rank 2 or higher with a shape of `(., num_boxes, 4)` representing bounding boxes where each box is of the format ``. stack (, boxes, boxes, boxes ], axis =- 1 ) def convert_to_xywh ( boxes ): """Changes the box format to center, width and height. Returns: swapped boxes with shape same as that of boxes. Arguments: boxes: A tensor with shape `(num_boxes, 4)` representing bounding boxes. The problem of the extreme foreground-background class imbalance.ĭef swap_xy ( boxes ): """Swaps order the of x and y coordinates of the boxes. Multiple scales and introduces a new loss, the Focal loss function, to alleviate RetinaNet uses a feature pyramid network to efficiently detect objects at Here in this example, we will implement RetinaNet,Ī popular single-stage detector, which is accurate and runs fast. Two-stage detectors are often more accurate but at theĬost of being slower. Object detection models can be broadly classified into "single-stage" and Image, and at the same time, classifying them into different categories. Here the model is tasked with localizing the objects present in an Object detection a very important problem in computer
Description: Implementing RetinaNet: Focal Loss for Dense Object Detection.
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