What is the purpose of using multiple anchors per feature map cell?

The purpose of using multiple anchors per feature map is to take into consideration/account the different shapes of object present in an image. For example, a car and human would need different anchor boxes shapes to correctly predict the bbox.

Why anchor boxes are used in Yolo?

What are anchor boxes? YOLO can work well for multiple objects where each object is associated with one grid cell. But in the case of overlap, in which one grid cell actually contains the centre points of two different objects, we can use something called anchor boxes to allow one grid cell to detect multiple objects.

What is an anchor in machine learning?

Anchor boxes are a technique used in some computer vision object detection algorithms to help identify objects of different shapes. Anchor boxes are hand-picked boxes of different height/width ratios (for 2-dimensional boxes) designed to match the relative ratios of the object classes being detected.

What is the minimum number of anchor box that is required?

It is typical to select between 4-10 anchor boxes to use as proposals over various locations in the image. Within the realm of computer vision, deep learning neural networks have excelled at image classification and object detection.

Why do we need anchor boxes?

The use of anchor boxes enables a network to detect multiple objects, objects of different scales, and overlapping objects.

What is the goal of object detection?

The main purpose of object detection is to identify and locate one or more effective targets from still image or video data. It comprehensively includes a variety of important techniques, such as image processing, pattern recognition, artificial intelligence and machine learning.

Why are anchor boxes used?

Does Yolo use anchor boxes?

In order to predict and localize many different objects in an image, most state of the art object detection models such as EfficientDet and the YOLO models start with anchor boxes as a prior, and adjust from there.

What are anchor boxes in faster RCNN?

Anchor boxes are nothing but some reference boxes placed at different positions in the image. k anchor boxes are generated for each pixel in our feature map(output of CNN). Thus the total number of anchor boxes is h*w*k(h*w is the output size of the feature map).

What is anchor image in Siamese network?

The variable “a” represents the anchor image, “p” represents a positive image and “n” represents a negative image. We know that the dissimilarity between a and p should be less than the dissimilarity between a and n,. Another variable called margin, which is a hyperparameter is added to the loss equation.

What are the applications of object detection?

It forms the basis of many other downstream computer vision tasks, for example, instance segmentation, image captioning, object tracking, and more. Specific object detection applications include pedestrian detection, people counting, face detection, text detection, pose detection, or number-plate recognition.

Where can object detection be used?

It is widely used in computer vision tasks such as image annotation, vehicle counting, activity recognition, face detection, face recognition, video object co-segmentation.

What are the disadvantages of using the default anchor box?

Using the default anchor box configuration can create predictors that are too specialized and objects that appear in the image may not achieve an IOU of 50\% with any of the anchor boxes. In this case, the neural network will never know these objects existed and will never learn to predict them.

Does the anchor box predict the type of object in images?

If the highest IOU is less than 40\%, then the anchor box should predict that there is no object. This works well in practice and the thousands of predictors do a very good job of deciding whether their type of object appears in an image.

Why is it important to tune anchor boxes correctly?

It is also one of the most important parameters you can tune for improved performance on your dataset. In fact, if anchor boxes are not tuned correctly, your neural network will never even know that certain small, large or irregular objects exist and will never have a chance to detect them.

Why is convolutional neural network so good at image classification?

“Convolutional Neural Network is very good at image classification”.This is one of the very widely known and well-advertised fact, but why is it so? The number of parameters in a neural network grows rapidly with the increase in the number of layers. This can make training for a model computationally heavy (and sometimes not feasible).