What is yolo

To understand the YOLO algorithm better, let us first understand the various other types of algorithms simultaneously. These algorithms use two-stage methods. Firstly, interested regions are selected.

Secondly, they are classified using Convolutional Neutral Networks by running predictions. This implements a single-step process wherein, in one run, the image is screened, and objects are located with the bounding boxes as well as their class is predicted in the same run. Hence, YOLO You Only Look Once Algorithm uses the regression-based technique, and as its name suggests, it detects and performs operations in a single run with the application of Artificial intelligence and Deep Learning.

The YOLO algorithms, unlike other algorithms, involve the splitting of the image into multiple cells and depending upon how many objects are covered in the image, multiple bounding boxes are predicted by each cell. This causes the creation of a large number of bounding boxes, and in this process, there may arise bounding boxes that do not contain any object at all or also intersected bounding boxes that share the same spaces of the image.

To get rid of this issue, a non-max suppression technique is used wherein such shared spaces are nullified, and also p c value is predicted to identify the boxes with no objects and ensure their removal.

The initial layers of Convolution help extract feature from the image, and the fully connected layers predict the output probabilities. It consists of 24 convolutional layers along with 2 fully connected layers. It is an Open Source neural network framework that is easy to install.

One reason for its rise to fame is its add-on app to Snapchat. FM, Sarahah, and Whisper. Recent media coverage regarding a tragic incident where a life was lost due to a result of cyberbullying on the app, meant that the app is currently unavailable to download in the UK.

There are no parental controls, privacy, or location settings neither is there any safety information available for parents as the app relies on the settings within Snapchat. Learn how to set privacy and safety settings on the Snapchat social platform to help your child have a safer experience.

Users can leave comments on videos posted so, this may leave your child open to receiving negative messages. Giving them guidance about what to do if they are cyberbullied or see others being cyberbullied will help them to keep themselves safe.

Check out the fine print. Yolo says it may access your contact list and will collect information about the content you post, including groups you belong to, interactions with friends, and live videos. Check out our parental controls for how to do this. Share this content on. Internet Matters Team June 17th, Average Precision is calculated as the area under a precision vs recall curve for a set of predictions. Recall is calculated as the ratio of the total predictions made by the model under a class with a total of existing labels for the class.

On the other hand, Precision refers to the ratio of true positives with respect to the total predictions made by the model. The area under the precision vs recall curve gives us the Average Precision per class for the model. The average of this value, taken over all classes, is termed as mean Average Precision mAP.

Each of these N grids is responsible for the detection and localization of the object it contains. Correspondingly, these grids predict B bounding box coordinates relative to their cell coordinates, along with the object label and probability of the object being present in the cell. This process greatly lowers the computation as both detection and recognition are handled by cells from the image, but—. It brings forth a lot of duplicate predictions due to multiple cells predicting the same object with different bounding box predictions.

YOLO achieves this by first looking at the probability scores associated with each decision and taking the largest one.

Following this, it suppresses the bounding boxes having the largest Intersection over Union with the current high probability bounding box. YOLO, as we know, predicts a single object per grid cell. While this makes the built model simpler, it creates issues when a single cell has more than one object, as YOLO can only assign a single class to the cell.

YOLOv2 gets rid of this limitation by allowing the prediction of multiple bounding boxes from a single cell. This is achieved by making the network predict 5 bounding boxes for each cell.

The number 5 is empirically derived as having a good trade-off between model complexity and prediction performance. DarkNet containing a total of 19 convolutional layers and 5 max-pooling layers is used as the backbone for the YOLOv2 architecture.

The object detection dataset that these models were trained on COCO has only 80 classes as compared to classification networks like ImageNet which has To enable the detection of many more classes, YOLO makes use of labels from both ImageNet and COCO, effectively merging the classification and detection tasks to only perform detection.

Since some classes of COCO can be referred to as superset classes of some classes of ImageNet, YOLO makes use of a hierarchical classification-based algorithm inspired by WordNet, where classes and their subclasses are represented in a tree-based fashion. While YOLOv2 is a superfast network, various alternatives that offer better accuracies—like Single Shot Detectors—have also entered the scene.