Contents
What loss function does Yolo use?
YOLO uses sum-squared error between the predictions and the ground truth to calculate loss. The loss function composes of: the classification loss. the localization loss (errors between the predicted boundary box and the ground truth).
What is grid cell in Yolo?
Grid Cell The idea of YOLO is to output a fixed number of dimension which is big enough to contain all the objects. For each grid cell, it predicts B boundary boxes and each box has a box confidence score, it detects one object only regardless of the number of boxes B, it predicts C conditional class probabilities.
How does Yolo medium work?
YOLO is based on the idea of segmenting an image into smaller images. The image is split into a square grid of dimensions S×S, like so: The coordinates `(x, y)` represent the location of the center of the predicted bounding box, and the width and height are fractions relative to the entire image size.
How is Yolo used to predict multiple bounding boxes?
Quoting the original paper: YOLO predicts multiple bounding boxes per grid cell. At training time we only want one bounding box predictor to be responsible for each object. We assign one predictor to be “responsible” for predicting an object based on which prediction has the highest current IOU with the ground truth.
What are the limitations of the Yolo framework?
Limitations Of YOLO: 1 Spatial constraints on bounding box predictions as each grid cell only predicts two boxes and can have only one class. 2 It is difficult to detect small objects that appear in groups. 3 It struggles to generalize objects in new or unusual aspect ratios as the model learns to predict bounding boxes from data itself.
How does the loss function work in Yolo?
In practice, it means that if no object is present on the grid cell, the loss function will not penalize it for a wrong class prediction, as we will see later. The network only predicts one set of class probabilities per cell, regardless of the number of boxes B. That makes S x S x C class probabilities in total
How are object detectors trained in Yolo V2?
For each specialization, in Yolo V2, the class probabilities of the object inside the box is trained to be predicted, as the confidence score, but conditionally on positive positions. Putting it all together for an example of 5 anchors, 20 object classes, the output of the network at each position can be decomposed into 3 parts: