> ## Documentation Index > Fetch the complete documentation index at: https://mintlify.com/cvat-ai/cvat/llms.txt > Use this file to discover all available pages before exploring further. # Serverless Functions Overview > Introduction to serverless functions in CVAT for AI-assisted annotation ## What are Serverless Functions? Serverless functions in CVAT are lightweight, containerized AI models that enable automatic and semi-automatic annotation capabilities. These functions run as independent microservices using Nuclio, a high-performance serverless framework, and can be invoked on-demand to accelerate annotation workflows. ## Function Types CVAT supports four types of serverless functions: ### Detectors Automatically detect and annotate objects in images without user input. Detectors scan entire frames and return bounding boxes, polygons, or masks for detected objects. **Use cases:** * Bulk annotation of similar objects across multiple frames * Initial annotation pass before manual refinement * Quality control and validation ### Interactors Semi-automatic annotation tools that require user input (points, boxes) to segment specific objects. Interactors provide precise control over which objects to annotate. **Use cases:** * Interactive object segmentation * Fine-grained annotation of complex shapes * Annotation of objects that are difficult to detect automatically ### Trackers Automatically track objects across video frames after initial annotation. Trackers maintain object identity throughout the sequence. **Use cases:** * Video annotation workflows * Temporal object tracking * Reducing manual frame-by-frame annotation effort ### ReID (Re-Identification) Match and link objects across frames based on visual similarity. ReID functions help maintain consistent object identities in multi-object tracking scenarios. **Use cases:** * Multi-object tracking * Cross-camera tracking * Object re-identification after occlusion ## Available Models CVAT includes several pre-configured serverless functions: | Model | Type | Framework | Description | Output Type | | -------------------------- | ---------- | -------------------- | ---------------------------------------------------- | ----------- | | **Segment Anything (SAM)** | Interactor | PyTorch | Meta's foundation model for interactive segmentation | Mask | | **YOLO v7** | Detector | ONNX | Fast object detection for 80 COCO classes | Rectangle | | **Mask R-CNN** | Detector | OpenVINO | Instance segmentation for 80 COCO classes | Mask | | **RetinaNet R101** | Detector | PyTorch (Detectron2) | Object detection with feature pyramid networks | Rectangle | | **Faster R-CNN** | Detector | TensorFlow | Object detection for COCO dataset | Rectangle | | **Face Detection 0205** | Detector | OpenVINO | Specialized face detection with attributes | Rectangle | | **Human Pose (HRNet)** | Detector | PyTorch (MMPose) | Keypoint detection for human pose estimation | Points | | **IOG** | Interactor | PyTorch | Inside-Outside Guidance for interactive segmentation | Polygon | | **TransT** | Tracker | PyTorch | Transformer-based visual object tracker | Rectangle | ### Model Details #### COCO Classes Many models (YOLO v7, Mask R-CNN, RetinaNet, Faster R-CNN) support the 80 COCO object classes including: * People and animals (person, cat, dog, horse, etc.) * Vehicles (car, truck, bus, bicycle, motorcycle, etc.) * Indoor objects (chair, couch, tv, laptop, etc.) * Food items (pizza, sandwich, apple, etc.) * Accessories (backpack, handbag, umbrella, etc.) ## Architecture Serverless functions in CVAT follow this architecture: ``` ┌─────────────┐ ┌──────────────┐ ┌─────────────┐ │ CVAT UI │────────▶│ CVAT Server │────────▶│ Nuclio │ │ │◀────────│ Lambda Mgr │◀────────│ Functions │ └─────────────┘ └──────────────┘ └─────────────┘ │ ▼ ┌─────────────┐ │ AI Model │ │ Container │ └─────────────┘ ``` 1. **CVAT UI**: User initiates annotation with a serverless function 2. **Lambda Manager**: Routes requests and manages function invocations 3. **Nuclio**: Serverless platform that hosts and scales functions 4. **AI Model Container**: Isolated environment running the ML model ## Performance Characteristics ### CPU vs GPU Most functions support both CPU and GPU execution: * **CPU**: Lower cost, suitable for batch processing and non-time-critical tasks * **GPU**: Faster inference, better for interactive tools and real-time annotation ### Scaling Nuclio automatically manages: * Function lifecycle and health checks * Request routing and load balancing * Resource allocation per function * Worker pool management (typically 2 workers per function) ## Integration with CVAT Serverless functions integrate with CVAT through: ### Automatic Annotation Batch processing mode for detectors: * Processes all frames in a task or job * Creates annotations automatically * Runs as a background job with progress tracking ### Interactive Tools Real-time mode for interactors: * Immediate feedback during annotation * Point-and-click or box-based interaction * Results displayed instantly in the UI ### Tracking Mode Video-specific workflows: * Initialize tracking with first-frame annotation * Automatically propagate to subsequent frames * Maintains object state across frames ## Next Steps Learn how to deploy serverless functions using Nuclio Create your own serverless functions