The capacity of computer networks to find desired sorts of items from an image/scene is referred to as object detection. The training data is provided in either XML or JSON files for object detection. Each representation has advantages and disadvantages. A dataset including all information about the items present in an image must be used to train an ML model to recognize things in an image. Annotation is a technique that creates a dataset that contains characteristics of all of the items in a picture. Annotation aids in the mapping of an item to its associated label by drawing a bounding box across the object. A rectangular rectangle termed a “bounding box” is used to symbolize each object-label mapping. The position of an item in an image is represented by bounding boxes, which are a set of coordinates or values.
This dataset contains standardized pictures for tasks like object detection and segmentation. These datasets are created with tools that adhere to established standards for evaluating and comparing various methodologies. The PASCAL VOC files were designated as the object detection benchmark in 2008. From 2005 through 2012 there were a series of object recognition competitions using a standardized file format for storing picture annotations. There were two key components to the PASCAL VOC challenge:
- A standardized assessment program and a publicly available dataset.
- An annual tournament, as well as a workshop, are held.
The major goals of this exercise were to determine the models’ capacity to accomplish the following tasks:
- Verify if an item is in the image by classifying it.
- Determine where the items in the picture are located.
With considerable adjustments to the dataset, this sequence of competitions came to a close in 2012. PASCAL VOC now offers standardized picture datasets for more than 20 distinct classes, which are often used for classification tasks.
Structure of Pascal VOC
- Folder – the folder that contains the dataset. This feature aids in the identification of annotated photographs inside a directory.
- Filename- the name of the picture file on which the data is tagged. This parameter indicates the annotated picture file’s relative path.
- Path- this is the absolute path of the picture file.
- Source- indicates where the file was originally stored in the database.
- Size- the width, height, and depth of a picture.
- Challenging object- indicates if an item is hard to recognize from a photograph where 0 is easy and is difficult.
The PASCAL VOC database is used to recognize and segment objects. Its storage as XML files allows us to readily alter datasets while maintaining a uniform format.