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# Object Detection Datasets Overview

Training a robust and accurate object detection model requires a comprehensive dataset. This guide introduces various formats of datasets that are compatible with the Ultralytics YOLO model and provides insights into their structure, usage, and how to convert between different formats.

## Supported Dataset Formats

### Ultralytics YOLO format

The Ultralytics YOLO format is a dataset configuration format that allows you to define the dataset root directory, the relative paths to training/validation/testing image directories or `*.txt` files containing image paths, and a dictionary of class names. Here is an example:

ultralytics/cfg/datasets/coco8.yaml

    # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

    # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
    # Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
    # Example usage: yolo train data=coco8.yaml
    # parent
    # ├── ultralytics
    # └── datasets
    #     └── coco8 ← downloads here (1 MB)

    # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
    path: coco8 # dataset root dir
    train: images/train # train images (relative to 'path') 4 images
    val: images/val # val images (relative to 'path') 4 images
    test: # test images (optional)

    # Classes
    names:
      0: person
      1: bicycle
      2: car
      3: motorcycle
      4: airplane
      5: bus
      6: train
      7: truck
      8: boat
      9: traffic light
      10: fire hydrant
      11: stop sign
      12: parking meter
      13: bench
      14: bird
      15: cat
      16: dog
      17: horse
      18: sheep
      19: cow
      20: elephant
      21: bear
      22: zebra
      23: giraffe
      24: backpack
      25: umbrella
      26: handbag
      27: tie
      28: suitcase
      29: frisbee
      30: skis
      31: snowboard
      32: sports ball
      33: kite
      34: baseball bat
      35: baseball glove
      36: skateboard
      37: surfboard
      38: tennis racket
      39: bottle
      40: wine glass
      41: cup
      42: fork
      43: knife
      44: spoon
      45: bowl
      46: banana
      47: apple
      48: sandwich
      49: orange
      50: broccoli
      51: carrot
      52: hot dog
      53: pizza
      54: donut
      55: cake
      56: chair
      57: couch
      58: potted plant
      59: bed
      60: dining table
      61: toilet
      62: tv
      63: laptop
      64: mouse
      65: remote
      66: keyboard
      67: cell phone
      68: microwave
      69: oven
      70: toaster
      71: sink
      72: refrigerator
      73: book
      74: clock
      75: vase
      76: scissors
      77: teddy bear
      78: hair drier
      79: toothbrush

    # Download script/URL (optional)
    download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip

Labels for this format should be exported to YOLO format with one `*.txt` file per image. If there are no objects in an image, no `*.txt` file is required. The `*.txt` file should be formatted with one row per object in `class x_center y_center width height` format. Box coordinates must be in normalized xywh format (from 0 to 1). If your boxes are in pixels, you should divide `x_center` and `width` by image width, and `y_center` and `height` by image height. Class numbers should be zero-indexed (start with 0).

The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`):

When using the Ultralytics YOLO format, organize your training and validation images and labels as shown in the COCO8 dataset example below.

#### Usage Example

Here's how you can use YOLO format datasets to train your model:

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)

    # Train the model
    results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

    # Start training from a pretrained *.pt model
    yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640

### Ultralytics NDJSON format

The NDJSON (Newline Delimited JSON) format provides an alternative way to define datasets for Ultralytics YOLO11 models. This format stores dataset metadata and annotations in a single file where each line contains a separate JSON object.

An NDJSON dataset file contains:

1. Dataset record (first line): Contains dataset metadata including task type, class names, and general information
2. Image records (subsequent lines): Contains individual image data including dimensions, annotations, and file paths

NDJSON Example

Dataset record (line 1)Image record (lines 2+)

    {
        "type": "dataset",
        "task": "detect",
        "name": "Example",
        "description": "COCO NDJSON example dataset",
        "url": "https://app.ultralytics.com/user/datasets/example",
        "class_names": { "0": "person", "1": "bicycle", "2": "car" },
        "bytes": 426342,
        "version": 0,
        "created_at": "2024-01-01T00:00:00Z",
        "updated_at": "2025-01-01T00:00:00Z"
    }

    {
        "type": "image",
        "file": "image1.jpg",
        "url": "https://www.url.com/path/to/image1.jpg",
        "width": 640,
        "height": 480,
        "split": "train",
        "annotations": {
            "boxes": [
                [0, 0.52481, 0.37629, 0.28394, 0.41832],
                [1, 0.73526, 0.29847, 0.19275, 0.33691]
            ]
        }
    }

Annotation formats by task:

- Detection: `"annotations": {"boxes": [[class_id, x_center, y_center, width, height], ...]}`
- Segmentation: `"annotations": {"segments": [[class_id, x1, y1, x2, y2, ...], ...]}`
- Pose: `"annotations": {"pose": [[class_id, x1, y1, v1, x2, y2, v2, ...], ...]}`
- OBB: `"annotations": {"obb": [[class_id, x_center, y_center, width, height, angle], ...]}`
- Classification: `"annotations": {"classification": [class_id]}`

#### Usage Example

To use an NDJSON dataset with YOLO11, simply specify the path to the `.ndjson` file:

Example

Python CLI

    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolo11n.pt")

    # Train using NDJSON dataset
    results = model.train(data="path/to/dataset.ndjson", epochs=100, imgsz=640)

    # Start training with NDJSON dataset
    yolo detect train data=path/to/dataset.ndjson model=yolo11n.pt epochs=100 imgsz=640

#### Advantages of NDJSON format

- Single file: All dataset information contained in one file
- Streaming: Can process large datasets line-by-line without loading everything into memory
- Cloud integration: Supports remote image URLs for cloud-based training
- Extensible: Easy to add custom metadata fields
- Version control: Single file format works well with git and version control systems

## Supported Datasets

Here is a list of the supported datasets and a brief description for each:

- Argoverse: A dataset containing 3D tracking and motion forecasting data from urban environments with rich annotations.
- COCO: Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
- LVIS: A large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
- COCO8: A smaller subset of the first 4 images from COCO train and COCO val, suitable for quick tests.
- COCO8-Grayscale: A grayscale version of COCO8 created by converting RGB to grayscale, useful for single-channel model evaluation.
- COCO8-Multispectral: A 10-channel multispectral version of COCO8 created by interpolating RGB wavelengths, useful for spectral-aware model evaluation.
- COCO128: A smaller subset of the first 128 images from COCO train and COCO val, suitable for tests.
- Global Wheat 2020: A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
- Objects365: A high-quality, large-scale dataset for object detection with 365 object categories and over 600K annotated images.
- OpenImagesV7: A comprehensive dataset by Google with 1.7M train images and 42k validation images.
- SKU-110K: A dataset featuring dense object detection in retail environments with over 11K images and 1.7 million bounding boxes.
- HomeObjects-3K: A dataset of indoor household items including beds, chairs, TVs, and more—ideal for applications in smart home automation, robotics, augmented reality, and room layout analysis.
- Construction-PPE: New 🚀: A dataset featuring construction site workers with labeled safety gear such as helmets, vests, gloves, boots, and goggles, including missing-equipment annotations like no_helmet, no_googles for real-world compliance monitoring.
- VisDrone: A dataset containing object detection and multi-object tracking data from drone-captured imagery with over 10K images and video sequences.
- VOC: The Pascal Visual Object Classes (VOC) dataset for object detection and segmentation with 20 object classes and over 11K images.
- xView: A dataset for object detection in overhead imagery with 60 object categories and over 1 million annotated objects.
- Roboflow 100: A diverse object detection benchmark with 100 datasets spanning seven imagery domains for comprehensive model evaluation.
- Brain-tumor: A dataset for detecting brain tumors includes MRI or CT scan images with details on tumor presence, location, and characteristics.
- African-wildlife: A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebras.
- Signature: A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.
- Medical-pills: A dataset featuring images of medical-pills, annotated for applications such as pharmaceutical quality assurance, pill sorting, and regulatory compliance.

### Adding your own dataset

If you have your own dataset and would like to use it for training detection models with Ultralytics YOLO format, ensure that it follows the format specified above under "Ultralytics YOLO format". Convert your annotations to the required format and specify the paths, number of classes, and class names in the YAML configuration file.

## Port or Convert Label Formats

### COCO Dataset Format to YOLO Format

You can easily convert labels from the popular COCO dataset format to the YOLO format using the following code snippet:

Example

Python

    from ultralytics.data.converter import convert_coco

    convert_coco(labels_dir="path/to/coco/annotations/")

This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The process transforms the JSON-based COCO annotations into the simpler text-based YOLO format, making it compatible with Ultralytics YOLO models.

Remember to double-check if the dataset you want to use is compatible with your model and follows the necessary format conventions. Properly formatted datasets are crucial for training successful object detection models.

## FAQ

### What is the Ultralytics YOLO dataset format and how to structure it?

The Ultralytics YOLO format is a structured configuration for defining datasets in your training projects. It involves setting paths to your training, validation, and testing images and corresponding labels. For example:

    # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

    # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
    # Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
    # Example usage: yolo train data=coco8.yaml
    # parent
    # ├── ultralytics
    # └── datasets
    #     └── coco8 ← downloads here (1 MB)

    # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
    path: coco8 # dataset root dir
    train: images/train # train images (relative to 'path') 4 images
    val: images/val # val images (relative to 'path') 4 images
    test: # test images (optional)

    # Classes
    names:
      0: person
      1: bicycle
      2: car
      3: motorcycle
      4: airplane
      5: bus
      6: train
      7: truck
      8: boat
      9: traffic light
      10: fire hydrant
      11: stop sign
      12: parking meter
      13: bench
      14: bird
      15: cat
      16: dog
      17: horse
      18: sheep
      19: cow
      20: elephant
      21: bear
      22: zebra
      23: giraffe
      24: backpack
      25: umbrella
      26: handbag
      27: tie
      28: suitcase
      29: frisbee
      30: skis
      31: snowboard
      32: sports ball
      33: kite
      34: baseball bat
      35: baseball glove
      36: skateboard
      37: surfboard
      38: tennis racket
      39: bottle
      40: wine glass
      41: cup
      42: fork
      43: knife
      44: spoon
      45: bowl
      46: banana
      47: apple
      48: sandwich
      49: orange
      50: broccoli
      51: carrot
      52: hot dog
      53: pizza
      54: donut
      55: cake
      56: chair
      57: couch
      58: potted plant
      59: bed
      60: dining table
      61: toilet
      62: tv
      63: laptop
      64: mouse
      65: remote
      66: keyboard
      67: cell phone
      68: microwave
      69: oven
      70: toaster
      71: sink
      72: refrigerator
      73: book
      74: clock
      75: vase
      76: scissors
      77: teddy bear
      78: hair drier
      79: toothbrush

    # Download script/URL (optional)
    download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip

Labels are saved in `*.txt` files with one file per image, formatted as `class x_center y_center width height` with normalized coordinates. For a detailed guide, see the COCO8 dataset example.

### How do I convert a COCO dataset to the YOLO format?

You can convert a COCO dataset to the YOLO format using the Ultralytics conversion tools. Here's a quick method:

    from ultralytics.data.converter import convert_coco

    convert_coco(labels_dir="path/to/coco/annotations/")

This code will convert your COCO annotations to YOLO format, enabling seamless integration with Ultralytics YOLO models. For additional details, visit the Port or Convert Label Formats section.

### Which datasets are supported by Ultralytics YOLO for object detection?

Ultralytics YOLO supports a wide range of datasets, including:

- Argoverse
- COCO
- LVIS
- COCO8
- Global Wheat 2020
- Objects365
- OpenImagesV7

Each dataset page provides detailed information on the structure and usage tailored for efficient YOLO11 training. Explore the full list in the Supported Datasets section.

### How do I start training a YOLO11 model using my dataset?

To start training a YOLO11 model, ensure your dataset is formatted correctly and the paths are defined in a YAML file. Use the following script to begin training:

Example

Python CLI

    from ultralytics import YOLO

    model = YOLO("yolo11n.pt")  # Load a pretrained model
    results = model.train(data="path/to/your_dataset.yaml", epochs=100, imgsz=640)

    yolo detect train data=path/to/your_dataset.yaml model=yolo11n.pt epochs=100 imgsz=640

Refer to the Usage section for more details on utilizing different modes, including CLI commands.

### Where can I find practical examples of using Ultralytics YOLO for object detection?

Ultralytics provides numerous examples and practical guides for using YOLO11 in diverse applications. For a comprehensive overview, visit the Ultralytics Blog where you can find case studies, detailed tutorials, and community stories showcasing object detection, segmentation, and more with YOLO11. For specific examples, check the Usage section in the documentation.

📅 Created 1 year ago ✏️ Updated 10 days ago

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