真实值图像和视频
Computer Vision Toolbox™ 提供了完整的工作流，用于从图像和视频生成真实值数据，以训练 AI 模型来执行目标检测、语义分割、实例分割、文本识别以及图像或视频分类等任务。您可以首先使用图像标注器和视频标注器以交互方式使用各种标签类型为数据添加注解。这些标签类型包括矩形、多边形、折线、场景标签和像素级标签。要开始标注图像集合，请参阅Get Started with the Image Labeler。要开始标注一段视频或一个图像序列，请参阅Get Started with the Video Labeler。
图像标注器和视频标注器支持手动、AI 辅助和自动注解，使您能够利用内置 AI 模型（如分割一切模型 (SAM) 和 Grounding DINO）来加速标注。有关详细信息，请参阅Get Started with AI-Assisted and Automated Labeling。您还可以集成自定义自动标注算法，以根据您的特定需求调整标注过程。有关详细信息，请参阅Create Custom Automation Algorithm for Labeling。
标注完成后，您可以导出注解数据并对其进行后处理，以创建 AI 模型的训练数据集。工具箱支持用于组织和管理已标注数据的工作流，可与分类、检测和分割任务的训练流程无缝集成。
对于协作项目，图像标注器包含用于管理基于团队的标注的特征，使您能够分配标注任务、审核注解、提供反馈以及跟踪多个参与者的进度。这使得扩大标注工作规模和保持大型数据集间的一致性变得更加容易。有关详细信息，请参阅Get Started with Team-Based Labeling。
类别
- 标注图像和视频
使用图像标注器和视频标注器标注图像和视频
- AI 辅助和自动标注
使用 SAM 和 Grounding DINO 等 AI 辅助工具自动标注，创建自定义自动标注算法
- 管理团队标注项目
使用图像标注器创建和管理协作标注项目，在团队成员之间分配标注和审核任务
- 使用真实值训练 AI 模型
对真实值数据进行预处理、增强和拆分，用于训练和评估 AI 模型
精选示例
Automatically Label Ground Truth Using Segment Anything Model
Produce pixel labels for semantic segmentation using the Segment Anything Model (SAM) in the 图像标注器 app. The SAM is an automatic segmentation technique that you can use to segment object regions to label with just a few clicks, or automatically segment the entire image and instantaneously create labels for selected regions. In this example, you interactively label pixels for semantic segmentation in two ways.
Automatically Label Ground Truth Using Vision-Language Model
Automatically label ground truth images for object detection using the Grounding DINO vision-language model (VLM).
Automate Ground Truth Polygon Labeling Using Grounded SAM Model
Combine Grounding DINO and the Segment Anything Model 2 (SAM 2) to automatically produce polygon labels using the Video Labeler app.
Automate Ground Truth Labeling for Semantic Segmentation
Use a pretrained semantic segmentation algorithm to segment the sky and a road in an image.
Automate Ground Truth Labeling for Instance Segmentation
Create an automation algorithm to automatically label data for instance segmentation using a pretrained SOLOv2 network in the Video Labeler app.
Automate Ground Truth Labeling for Object Detection
Create an automation algorithm to automatically label data for object detection using a pretrained object detector.
Automate Ground Truth Labeling for OCR
Automate the labeling of text for OCR training and evaluation.
Automate Labeling of Objects in Video Using RAFT Optical Flow
Use a pretrained RAFT optical flow estimation network to propagate a predefined object mask from one frame to the next in a video sequence.
Export Ground Truth Object to Custom and COCO JSON Files
Export a ground truth object to a custom data format JavaScript Object Notation (JSON) file and to a COCO data format JSON file.
Convert Image Labeler Polygons to Labeled Blocked Image for Semantic Segmentation
Convert polygon labels stored in a groundTruth object into a labeled blocked image for semantic segmentation workflows.
