> For the complete documentation index, see [llms.txt](https://labelfi-1.gitbook.io/labelfi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://labelfi-1.gitbook.io/labelfi/basics/labelfi-data-labeling-services/4.1-image-annotation.md).

# 4.1 Image Annotation

Image annotation is critical for training machine learning models, especially in

computer vision. Labelfi employs several advanced techniques to ensure precise image labeling, which includes:

* Bounding Boxes: Drawing rectangular borders around objects to identify them in an image. This method is widely used for object detection tasks.
* Polygon Annotation: Creating more flexible and accurate shapes around objects, which is particularly useful for irregular shapes or fine object boundaries【12】.
* Instance and Semantic Segmentation: Segmenting images to label each pixel of an object or category, which provides more granular detail and is valuable in autonomous driving and medical image analysis.
* Keypoint and Landmark Annotation: Identifying specif

Labelfi's AI-augmented annotation process allows for a combination of human and machine-driven efforts, increasing both speed and accuracy in image annotation tasks.