If your labeling tool exported annotations in the
VoTTformat, but you’re trying to use a
model that needs
YOLOv4 Pytorch annotations, we’ve got you covered. You can convert data between these formats for free in 3 clicks with Roboflow.
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 free workspace on the Public plan.
Once your account has been created, click Create New Project.
To create annotations in VoTT JSON format and ensure you are following all of the best practices, follow our VoTT tutorial.
Next, click "Generate New Version" to generate a new version of your dataset:
You can then apply any preprocessing or augmentation steps to your dataset:
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:
Congratulations, you have successfully converted your dataset from
VoTT JSON
format to
YOLOv4 PyTorch TXT
format!
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.
Ready to use your new
YOLOv4 Pytorch
dataset? Great!
To learn more about YOLOv4 PyTorch, check it out in our model library.
Here are some compatible models:
Yes! It is free to convert
VoTT 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.