Product
Docs & Support
Pricing
Contact Sales
Sign In
Try It Now

PRODUCT

DOCS & Support

INDUSTRIES

Sign InTry It Now
Formats
CreateML JSON
Convert to
YOLO Darknet TXT
CreateML JSON
Apple's CreateML and Turi Create tools need a special JSON format for object detection tasks.
YOLO Darknet TXT
The favored annotation format of the Darknet family of models.

How To Convert CreateML JSON to YOLO Darknet TXT

If your labeling tool exported annotations in the

CreateML

format, but you’re trying to use a

YOLOv4 Darknet

model that needs

YOLO Darknet

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

Step 1: Create a Free Roboflow Account

Roboflow is the universal conversion tool for computer vision annotation formats. It's completely free for your first 1,000 images. You'll need an account to convert your dataset.

Create Your Free Roboflow Account

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
CreateML JSON
images and annotations into the upload space.
Upload Data To Roboflow

Training a no-code object detection model with CreateML is easy, just follow our CreateML tutorial.

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

YOLO Darknet TXT

.

Congratulations, you have successfully converted your dataset from

CreateML JSON

format to

YOLO Darknet TXT

format!

Next Steps

Ready to use your new

YOLO Darknet

dataset?

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

Here are some compatible models:

YOLOv4 Darknet

YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in Darknet.

YOLOv4 Tiny

The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset