How To Convert YOLO Darknet TXT to COCO JSON

Formats



YOLO Darknet TXT



Convert to

COCO JSON

YOLO Darknet TXT

The favored annotation format of the Darknet family of models.





COCO JSON

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

How To Convert YOLO Darknet TXT to COCO JSON

If your labeling tool exported annotations in the

YOLO Darknet

format, but you’re trying to use a

EfficientDet

model that needs

COCO

annotations, we’ve got you covered. You can convert those in 3 clicks with Roboflow.

Step 1: Create a Free Roboflow Public Workspace

Roboflow is the 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 workspace on the Public plan.

Create Roboflow Workspace

Step 2: Upload your data into Roboflow

Once your account has been created, click Create Dataset.

Upload your data to Roboflow by dragging and dropping your

YOLO Darknet TXT

images and annotations into the upload space.

You might be looking to use YOLOv4 on your own dataset.

Step 3: Generate Dataset Version

Next, you can choose Preprocessing and Augmentation options for your dataset version and then click Generate.

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. You can also choose which format you would like to export. Choose

COCO JSON

Congratulations, you have successfully converted your dataset from

YOLO Darknet TXT

format to

COCO JSON

format!

Next Steps

Ready to use your new

COCO

dataset?

To learn how to create COCO JSON yourself from scratch, see our CVAT (object detection annotation tool) tutorial.

Here are some compatible models:

EfficientDet

EfficientDet achieves the best performance in the fewest training epochs among object detection model architectures, making it a highly scalable architecture especially when operating with limited compute.

Detectron2

Detectron2 is model zoo of it's own for computer vision models written in PyTorch. Detectron2 includes all the models that were available in the original Detectron, such as Faster R-CNN, Mask R-CNN, RetinaNet, and DensePose. It also features several new models, including Cascade R-CNN, Panoptic FPN, and TensorMask.