Convert annotation formats

How to Convert YOLO to COCO JSON

In this guide, we show you how to convert data between the YOLO and COCO formats for free. You can use your converted data to train YOLOv8 Pose Estimation models and other models that support the COCO format.

YOLO

YOLO PyTorch TXT is a format commonly used by YOLO vision models like YOLO26, YOLO11, YOLOv8, and YOLOv5.

Below, learn the structure of YOLO.

Each image has one txt file with a single line for each bounding box. The format of each row is

class_id center_x center_y width height

where fields are space delimited, and the coordinates are normalized from zero to one.

Note: To convert to normalized xywh from pixel values, divide x (and width) by the image's width and divide y (and height) by the image's height.

001.txt
1 0.617 0.3594420600858369 0.114 0.17381974248927037
1 0.094 0.38626609442060084 0.156 0.23605150214592274
1 0.295 0.3959227467811159 0.13 0.19527896995708155
1 0.785 0.398068669527897 0.07 0.14377682403433475
1 0.886 0.40879828326180256 0.124 0.18240343347639484
1 0.723 0.398068669527897 0.102 0.1609442060085837
1 0.541 0.35085836909871243 0.094 0.16952789699570817
1 0.428 0.4334763948497854 0.068 0.1072961373390558
1 0.375 0.40236051502145925 0.054 0.1351931330472103
1 0.976 0.3927038626609442 0.044 0.17167381974248927

The `data.yaml` file contains configuration values used by the model to locate images and map class names to class_id's.

data.yaml
train: ../train/images
val: ../valid/images

nc: 3
names: ['head', 'helmet', 'person']

COCO JSON

COCO is a common JSON format used for machine learning because the dataset it was introduced with has become a common benchmark.

Below, learn the structure of COCO JSON.

coco.jsonJSON
{
    "info": {
        "year": "2020",
        "version": "1",
        "description": "Exported from roboflow.ai",
        "contributor": "Roboflow",
        "url": "https://app.roboflow.ai/datasets/hard-hat-sample/1",
        "date_created": "2000-01-01T00:00:00+00:00"
    },
    "licenses": [
        {
            "id": 1,
            "url": "https://creativecommons.org/publicdomain/zero/1.0/",
            "name": "Public Domain"
        }
    ],
    "categories": [
        {
            "id": 0,
            "name": "Workers",
            "supercategory": "none"
        },
        {
            "id": 1,
            "name": "head",
            "supercategory": "Workers"
        },
        {
            "id": 2,
            "name": "helmet",
            "supercategory": "Workers"
        },
        {
            "id": 3,
            "name": "person",
            "supercategory": "Workers"
        }
    ],
    "images": [
        {
            "id": 0,
            "license": 1,
            "file_name": "0001.jpg",
            "height": 275,
            "width": 490,
            "date_captured": "2020-07-20T19:39:26+00:00"
        }
    ],
    "annotations": [
        {
            "id": 0,
            "image_id": 0,
            "category_id": 2,
            "bbox": [
                45,
                2,
                85,
                85
            ],
            "area": 7225,
            "segmentation": [],
            "iscrowd": 0
        },
        {
            "id": 1,
            "image_id": 0,
            "category_id": 2,
            "bbox": [
                324,
                29,
                72,
                81
            ],
            "area": 5832,
            "segmentation": [],
            "iscrowd": 0
        }
    ]
}

Step 1: Create a free Roboflow public workspace

Roboflow is a universal conversion tool for computer vision annotation formats. The Public plan is the best way for those exploring personal projects, class assignments, and other experiments to try Roboflow. To convert your dataset, start by creating a free workspace on the Public plan.

Create Roboflow Workspace

Step 2: Upload your data into Roboflow

Once your account has been created, click Create New Project. (opens in a new tab)

Roboflow's create project form, with Object Detection selected as the project type

Upload your data to Roboflow by dragging and dropping your YOLO images and annotations into the upload space.

Roboflow's upload page, with a drag-and-drop area for images, annotations, videos and PDFs

Step 3: Generate Dataset Version

Next, click "Generate New Version" to generate a new version of your dataset:

Roboflow's Generate a Dataset Version page, showing the source images and train/test split for a new version

You can then apply any preprocessing or augmentation steps to your dataset:

The augmentation step of a new dataset version, with 90° Rotate and Brightness added

Step 4: Export Dataset Version

After generating, you will be prompted to Export your dataset. You can choose to receive your dataset as a .zip file or a curl download link. Choose COCO JSON when asked in what format you want to export your data. You will see a dropdown with various options like this:

Roboflow's Download dialog, with the export format list open and COCO highlighted

Congratulations, you have successfully converted your dataset from YOLO format to COCO JSON format!

Video Tutorial

Convert Computer Vision Annotation Formats Tutorial

Want to dive deeper into converting annotation formats with Roboflow? In the tutorial below, we explore how to convert your data in the Roboflow dashboard. We also discuss rejecting annotations and train-test-validation splits.

Frequently Asked Questions

Yes! It is free to convert YOLO data into the COCO JSON format on the Roboflow platform.

If you have between a few and a few thousand images, converting data between these formats will be quick. But, the time it takes to convert between data formats increases with the more images you have.

Show guide

Convert other formats to COCO JSON

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