How To Convert VGG Image Annotator JSON to YOLOv4 PyTorch TXT

Formats



VGG Image Annotator JSON



Convert to

YOLOv4 PyTorch TXT

VGG Image Annotator JSON

The VGG Image Annotator tool's JSON format.





YOLOv4 PyTorch TXT

A format used with the PyTorch port of YOLO v4.

How To Convert VGG Image Annotator JSON to YOLOv4 PyTorch TXT

If your labeling tool exported annotations in the

VIA JSON

format, but you’re trying to use a

YOLOv4 PyTorch

model that needs

YOLOv4 Pytorch

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

VGG Image Annotator JSON

images and annotations into the upload space.

Learn more about how to create these annotations for your object detection dataset in our VGG Image Annotator 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

YOLOv4 PyTorch TXT

Congratulations, you have successfully converted your dataset from

VGG Image Annotator JSON

format to

YOLOv4 PyTorch TXT

format!

Next Steps

Ready to use your new

YOLOv4 Pytorch

dataset?

To learn more about YOLOv4 PyTorch, check it out in our model library.

Here are some compatible models:

YOLOv4 PyTorch

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 PyTorch.