What are the data labeling methods?

There are four methods of data labeling: classification, bounding box, labeling and marking.

1, classification

Classification is the process of dividing data into different categories or sets of categories. Journalists need to classify data samples into one or more predefined categories or labels. This method is often used in image classification, text classification and sentiment analysis. For example, in image classification, the annotator can classify the objects in the image into different categories, such as cats, dogs, cars and so on.

2. Frame method (bounding box)

Frame method is a method to mark the position of a target object in an image or video. The annotator needs to draw a bounding box to frame the position of the target object. This method is often used in target detection, target tracking and computer vision. For example, in the field of autonomous driving, the frame method can be used to mark the position of the vehicle on the road.

3. Notes.

Annotations include adding detailed text or graphic annotations to data to provide additional information about the data. This method is usually used for text data, map data and medical images. For example, in medical images, annotations can be used to mark the boundaries and features of tumors.

Step 4 sharpen your point

Tagging is a way to associate tags or keywords with data. Taggers needs to add descriptive tags to the data for searching and classifying. This method is usually used for text data, audio data and social media content. For example, on social media, users can tag their posts so that other users can find related content more easily.

Matters needing attention in data labeling

1. Clearly define labels: Before starting labeling, make sure to clearly define the labels and standards of data. Labels should be clear and consistent to avoid ambiguity and confusion.

2. Training of marking personnel: Provide sufficient training for marking personnel to make them understand the requirements and standards of the task. Labelers need to know how to label and master the professional knowledge in a specific field or task.

3. Labeling specifications: formulate labeling specifications and clarify the details of data labeling, including the definition of labels, labeling methods, error handling and uncertainty handling. Specifications should be operational guidelines.

4. Randomness of data samples: When classifying or labeling objects, ensure that the selection of data samples is random to avoid deviation and over-fitting.

5. Quality control: Implement quality control process to monitor and evaluate the quality of labels. This may include reviewing annotation examples, cross-validation, and feedback loops.

6. Consistency of labeling: Consistency of different labeling personnel is the key. Use multiple labelers to label independently, and then calculate the consistency between labels to evaluate the quality.