Image annotation is essential for training object detection models. It involves labeling objects in images with bounding boxes, polygons, or other region-based annotations and assigning class labels to each object. Annotated data serves as the ground truth for supervised learning, enabling the model to learn object locations and classifications. Without accurate annotations, the model cannot generalize well to new data, resulting in poor performance. Image annotation is critical in applications like autonomous driving, where precise detection of pedestrians, vehicles, and road signs is essential for safety. Tools like Labelbox, CVAT, and RectLabel facilitate the annotation process.
What's the purpose of image annotation in object detection?

- Natural Language Processing (NLP) Advanced Guide
- How to Pick the Right Vector Database for Your Use Case
- Getting Started with Milvus
- Vector Database 101: Everything You Need to Know
- The Definitive Guide to Building RAG Apps with LlamaIndex
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does benchmarking assess data freshness?
Benchmarking is a systematic process used to evaluate and compare the performance of data processing systems, including
How do I create an API to interact with LlamaIndex?
To create an API that interacts with LlamaIndex, the first step involves setting up the environment where your API will
What is the significance of IO throughput in benchmarking?
IO throughput is crucial in benchmarking because it measures how well a system can handle input and output operations ov