What is Auto Bounding Box
Analytics Section
Try Auto Bounding Box at Labellerr
Auto Bounding Box, also known as object detection, is a technique used in computer vision to automatically locate and draw a bounding box around specific objects or regions of interest in an image or video. This is typically done using algorithms such as YOLO, R-CNN, and RetinaNet, which are trained on a dataset of labeled images to learn the characteristics and features of the objects of interest. Once trained, these algorithms can be used to detect and locate the objects in new, unseen images or videos. The goal of auto bounding box is to accurately and efficiently identify and locate specific objects within an image or video.
Go to the settings section in the header and click on the datasets in the sidebar
Then click on View Dataset on whichever dataset you want to classify
Click on AutoLabel
Select classification in the dialog box and enter all the labels on the basis of which you want to classify. Click on Detect Labels
Labels will be rendered on the files where detected
In the right side, an analytics section will appear. It will show Total Count of Bounding Boxes and their respective distribution. There, the user can Edit and Reset the Labels too
