> ## 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. # Supported Dataset Formats > Complete list of annotation formats supported by CVAT for import and export CVAT supports over 30 dataset formats for import and export, making it compatible with most computer vision workflows and frameworks. All format conversions are powered by the [Datumaro](https://github.com/cvat-ai/datumaro) framework. ## Format Overview The table below shows all supported formats with import/export capabilities: | Format | Import | Export | Use Case | | ---------------------------------------------------- | ------ | ------ | -------------------------------------------- | | [CVAT for images](#cvat-formats) | ✔️ | ✔️ | CVAT native format for image annotation | | [CVAT for video](#cvat-formats) | ✔️ | ✔️ | CVAT native format for video annotation | | [Datumaro](#datumaro) | ✔️ | ✔️ | Universal format with full feature support | | [PASCAL VOC](#pascal-voc) | ✔️ | ✔️ | Object detection, classification | | [PASCAL VOC Segmentation](#pascal-voc) | ✔️ | ✔️ | Semantic segmentation | | [YOLO](#yolo-formats) | ✔️ | ✔️ | Darknet YOLO object detection | | [MS COCO Object Detection](#coco-formats) | ✔️ | ✔️ | Object detection with MS COCO | | [MS COCO Keypoints](#coco-formats) | ✔️ | ✔️ | Keypoint/pose estimation | | [Cityscapes](#cityscapes) | ✔️ | ✔️ | Urban scene segmentation | | [MOT](#mot-formats) | ✔️ | ✔️ | Multi-object tracking | | [MOTS PNG](#mot-formats) | ✔️ | ✔️ | Multi-object tracking with segmentation | | [LabelMe](#labelme) | ✔️ | ✔️ | General-purpose annotation | | [ImageNet](#imagenet) | ✔️ | ✔️ | Image classification | | [CamVid](#camvid) | ✔️ | ✔️ | Semantic segmentation for autonomous driving | | [WIDER Face](#wider-face) | ✔️ | ✔️ | Face detection | | [VGGFace2](#vggface2) | ✔️ | ✔️ | Face recognition | | [Market-1501](#market-1501) | ✔️ | ✔️ | Person re-identification | | [ICDAR13/15](#icdar) | ✔️ | ✔️ | Text detection and recognition | | [Open Images V6](#open-images) | ✔️ | ✔️ | Large-scale object detection | | [KITTI](#kitti-formats) | ✔️ | ✔️ | Autonomous driving benchmarks | | [KITTI Raw](#kitti-formats) | ✔️ | ✔️ | Raw KITTI sensor data | | [LFW](#lfw) | ✔️ | ✔️ | Face verification | | [Supervisely Point Cloud](#supervisely-point-cloud) | ✔️ | ✔️ | 3D point cloud annotation | | [Ultralytics YOLO Detection](#ultralytics-yolo) | ✔️ | ✔️ | YOLOv8+ object detection | | [Ultralytics YOLO OBB](#ultralytics-yolo) | ✔️ | ✔️ | Oriented bounding boxes | | [Ultralytics YOLO Segmentation](#ultralytics-yolo) | ✔️ | ✔️ | Instance segmentation | | [Ultralytics YOLO Pose](#ultralytics-yolo) | ✔️ | ✔️ | Pose estimation | | [Ultralytics YOLO Classification](#ultralytics-yolo) | ✔️ | ✔️ | Image classification | ## Format Details ### CVAT Formats **CVAT for images 1.1** and **CVAT for video 1.1** are CVAT's native XML-based formats. **Features:** * Full support for all CVAT annotation types * Preserves all metadata and attributes * Tracks, shapes, tags, and skeleton annotations * Best for backup and transfer between CVAT instances **When to use:** * Backing up CVAT projects * Migrating tasks between CVAT servers * When you need complete annotation preservation **Export example:** ```python theme={null} task.export_dataset( format_name="CVAT for images 1.1", filename="cvat_backup.zip" ) ``` See the CVAT XML format specification in the [CVAT GitHub repository](https://github.com/cvat-ai/cvat/blob/develop/cvat/apps/dataset_manager/formats/cvat.py) for details. ### Datumaro **Datumaro** is a universal dataset framework providing lossless conversion between formats. **Features:** * Supports all CVAT annotation types * Python-based dataset manipulation * Format conversion and validation * Dataset versioning and comparison **When to use:** * Complex dataset transformations * Converting between incompatible formats * Dataset analysis and statistics * Building custom ML pipelines **Export example:** ```python theme={null} task.export_dataset( format_name="Datumaro 1.0", filename="datumaro_dataset.zip", include_images=True ) ``` Learn more: [Datumaro documentation](https://github.com/cvat-ai/datumaro) ### PASCAL VOC **PASCAL VOC** is a classic format for object detection and segmentation. **Features:** * XML annotation files * Bounding boxes with classes * Segmentation masks (separate format) * Attributes stored in XML **When to use:** * Object detection with classic frameworks * Academic research and benchmarks * Simple detection tasks **Structure:** ``` dataset/ ├── Annotations/ │ ├── image1.xml │ └── image2.xml ├── JPEGImages/ │ ├── image1.jpg │ └── image2.jpg └── ImageSets/ └── Main/ └── train.txt ``` **Export example:** ```bash theme={null} cvat-cli task export-dataset \ --format "PASCAL VOC 1.1" \ --output voc_dataset.zip \ 123 ``` ### YOLO Formats CVAT supports multiple YOLO format variants: #### YOLO 1.1 (Darknet) Original YOLO format with text-based annotations. **Features:** * One .txt file per image * Normalized bounding box coordinates * Class ID, center\_x, center\_y, width, height * Requires obj.names and obj.data files **Format:** ```text theme={null} # image1.txt 0 0.5 0.5 0.3 0.4 1 0.2 0.3 0.1 0.2 ``` **Export example:** ```python theme={null} task.export_dataset( format_name="YOLO 1.1", filename="yolo_dataset.zip" ) ``` #### Ultralytics YOLO Modern YOLO format (YOLOv8+) with YAML configuration. **Variants:** * **Detection**: Bounding boxes for object detection * **Segmentation**: Polygon annotations for instance segmentation * **OBB**: Oriented bounding boxes for rotated objects * **Pose**: Keypoint annotations for pose estimation * **Classification**: Image-level labels **Structure:** ``` dataset/ ├── data.yaml ├── train/ │ ├── images/ │ └── labels/ └── valid/ ├── images/ └── labels/ ``` **Export example:** ```python theme={null} task.export_dataset( format_name="Ultralytics YOLO Detection 1.0", filename="yolov8_dataset.zip" ) ``` Learn more: [Ultralytics YOLO formats](https://docs.ultralytics.com/datasets/) ### COCO Formats **MS COCO** is an industry-standard format for complex annotations. #### COCO Object Detection **Features:** * JSON-based annotations * Bounding boxes with categories * Image metadata (size, file name) * Supports attributes as custom fields * Crowd annotations for groups **Structure:** ```json theme={null} { "images": [{"id": 1, "file_name": "image1.jpg", "width": 640, "height": 480}], "annotations": [{"id": 1, "image_id": 1, "category_id": 1, "bbox": [x, y, w, h]}], "categories": [{"id": 1, "name": "person", "supercategory": "human"}] } ``` **When to use:** * Training modern detection models (Faster R-CNN, YOLO, etc.) * Complex multi-class detection tasks * Integration with popular ML frameworks #### COCO Keypoints **Features:** * Keypoint annotations for pose estimation * Skeleton definitions * Visibility flags per keypoint * Compatible with pose estimation models **Export example:** ```python theme={null} task.export_dataset( format_name="COCO Keypoints 1.0", filename="coco_keypoints.zip" ) ``` Learn more: [COCO format specification](http://cocodataset.org/#format-data) ### Cityscapes **Cityscapes** format for urban scene understanding. **Features:** * Semantic segmentation masks * Instance segmentation annotations * 19 standard classes for street scenes * Polygons and pixel-level masks **When to use:** * Autonomous driving applications * Urban scene segmentation * Street-level computer vision **Export example:** ```python theme={null} task.export_dataset( format_name="Cityscapes 1.0", filename="cityscapes_dataset.zip" ) ``` Learn more: [Cityscapes dataset](https://www.cityscapes-dataset.com/) ### MOT Formats **MOT (Multiple Object Tracking)** formats for tracking tasks. #### MOT 1.1 **Features:** * Track annotations over time * CSV-based format * Frame number, track ID, bounding box * Compatible with MOT challenge **Format:** ```csv theme={null} # frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, visibility 1, 1, 100, 50, 30, 40, 1, 1, 1 2, 1, 105, 52, 30, 40, 1, 1, 1 ``` #### MOTS PNG **Features:** * Instance segmentation tracks * PNG masks for each frame * Pixel-level tracking annotations **Export example:** ```bash theme={null} cvat-cli task export-dataset \ --format "MOT 1.1" \ --output mot_tracks.zip \ 456 ``` ### LabelMe **LabelMe 3.0** format for general-purpose annotation. **Features:** * JSON annotations per image * Polygons, rectangles, and points * Flexible attribute system * Web-based annotation tool compatible **When to use:** * General object detection and segmentation * Research projects * Legacy LabelMe tool compatibility **Export example:** ```python theme={null} task.export_dataset( format_name="LabelMe 3.0", filename="labelme_annotations.zip" ) ``` ### ImageNet **ImageNet** format for image classification. **Features:** * Directory-based class organization * Image-level labels only * Standard classification dataset structure **Structure:** ``` dataset/ ├── train/ │ ├── class1/ │ │ ├── img1.jpg │ │ └── img2.jpg │ └── class2/ │ └── img3.jpg └── val/ └── ... ``` **When to use:** * Image classification tasks * Transfer learning * Training classification networks ### CamVid **CamVid** format for video segmentation. **Features:** * Semantic segmentation for video * 11 or 32 predefined classes * Per-frame segmentation masks **When to use:** * Video segmentation tasks * Autonomous driving research * Sequential scene understanding ### WIDER Face **WIDER Face** format for face detection. **Features:** * Face bounding boxes * Occlusion and pose attributes * Specialized for face detection benchmarks **When to use:** * Face detection model training * Benchmarking face detectors * Large-scale face recognition ### VGGFace2 **VGGFace2** format for face recognition. **Features:** * Face identity labels * Bounding boxes and landmarks * Identity-based organization **When to use:** * Face recognition training * Face verification tasks * Identity classification ### Market-1501 **Market-1501** format for person re-identification. **Features:** * Person bounding boxes * Identity labels across cameras * Track IDs for same person **When to use:** * Person re-identification * Multi-camera tracking * Pedestrian recognition ### ICDAR **ICDAR13/15** formats for text detection. **Features:** * Text region bounding boxes or polygons * Transcription labels * Oriented text support **When to use:** * Scene text detection * OCR training data * Text recognition tasks ### Open Images **Open Images V6** format for large-scale detection. **Features:** * Hierarchical label taxonomy * Image-level and object-level labels * Relationship annotations * Attributes and groups **When to use:** * Large-scale detection tasks * Multi-label classification * Complex object relationships ### KITTI Formats **KITTI** formats for autonomous driving. #### KITTI Detection **Features:** * 3D bounding boxes * Object detection in driving scenes * Occlusion and truncation flags #### KITTI Raw **Features:** * Raw sensor data format * Calibration information * Multi-modal data (camera, LiDAR) **When to use:** * Autonomous driving research * 3D object detection * Sensor fusion tasks ### LFW **Labeled Faces in the Wild** format. **Features:** * Face verification pairs * Identity labels * Standard face recognition benchmark **When to use:** * Face verification * Face recognition benchmarking ### Supervisely Point Cloud **Supervisely Point Cloud Format** for 3D annotation. **Features:** * 3D bounding boxes * Point cloud annotations * 3D object detection **When to use:** * LiDAR data annotation * 3D object detection * Autonomous driving 3D perception ### Ultralytics YOLO See [YOLO Formats](#yolo-formats) above for detailed information on Ultralytics YOLO variants. ## Choosing the Right Format Consider these factors when selecting a format: ### By Task Type * **Object Detection**: COCO, YOLO, Pascal VOC, Open Images * **Instance Segmentation**: COCO, YOLO Segmentation, MOTS * **Semantic Segmentation**: Cityscapes, CamVid, Pascal VOC Segmentation * **Classification**: ImageNet, YOLO Classification * **Pose/Keypoints**: COCO Keypoints, YOLO Pose * **Tracking**: MOT, MOTS PNG * **3D Detection**: KITTI, Supervisely Point Cloud * **Face Tasks**: WIDER Face, VGGFace2, LFW * **Text Detection**: ICDAR ### By Framework * **PyTorch**: COCO, YOLO, ImageNet * **TensorFlow**: COCO, Pascal VOC * **Darknet**: YOLO 1.1 * **Ultralytics**: Ultralytics YOLO variants * **MMDetection**: COCO * **Detectron2**: COCO ### By Complexity * **Simple**: ImageNet, YOLO * **Moderate**: Pascal VOC, LabelMe * **Complex**: COCO, Open Images, Datumaro ## Format Limitations Some formats have specific limitations: * **YOLO**: Only supports bounding boxes or polygons (depending on variant) * **ImageNet**: Only supports image-level classification * **Pascal VOC**: Limited attribute support compared to CVAT * **COCO**: Polygons only (no ellipses without conversion) * **MOT**: Primarily for tracking, limited object attributes For format-specific conversions and workarounds, see [Format Conversion](/datasets/format-conversion). ## Next Steps * [Import & Export](/datasets/import-export) - Learn how to use these formats * [Format Conversion](/datasets/format-conversion) - Convert between formats * [Datumaro Documentation](https://github.com/cvat-ai/datumaro) - Advanced dataset operations