Convert annotation formats

How To Convert COCO JSON to YOLOv4 PyTorch TXT

In this guide, we show you how to convert data between the COCO and YOLOv4 Pytorch formats for free. You can use your converted data to train YOLOv4 PyTorch models and other models that support the YOLOv4 Pytorch format.

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
        }
    ]
}

YOLOv4 PyTorch TXT

A format used with the PyTorch port of YOLO v4.

Below, learn the structure of YOLOv4 PyTorch TXT.

yolov4.pytorch.txt
000049.jpg 128,168,233,275,1 158,475,290,601,1
000080.jpg 45,2,130,87,1 324,29,396,110,1 269,56,325,120,1 120,12,178,82,1 31,36,60,72,1 315,15,373,66,1 454,1,489,113,1
000038.jpg 43,46,100,108,1 244,51,304,112,1
000025.jpg 64,19,180,140,1 204,75,286,177,1 340,0,468,138,1
000087.jpg 228,41,292,108,1
000026.jpg 95,0,167,63,1 167,45,231,123,1
000068.jpg 179,4,243,88,1 40,85,76,122,1 69,64,115,108,1 2,110,29,144,1
000018.jpg 128,38,149,61,1
000037.jpg 47,22,202,199,1
000020.jpg 95,32,123,65,1 149,93,176,120,1
_classes.txt
head
helmet
person

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 COCO JSON 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 YOLOv4 PyTorch TXT 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 COCO JSON format to YOLOv4 PyTorch TXT 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 COCO JSON data into the YOLOv4 PyTorch TXT 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 COCO JSON to other formats

Convert other formats to YOLOv4 PyTorch TXT

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