How To Convert Supervisely JSON to YOLOv5 PyTorch TXT

Formats



Supervisely JSON



Convert to

YOLOv5 PyTorch TXT

Supervisely JSON

Supervise.ly offers a computer vision platform and uses a proprietary JSON-based annotation format.





YOLOv5 PyTorch TXT

A modified version of YOLO Darknet annotations that adds a YAML file for model config.

How To Convert Supervisely JSON to YOLOv5 PyTorch TXT

If your labeling tool exported annotations in the

Supervise.ly

format, but you’re trying to use a

YOLOv5

model that needs

YOLOv5

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

Supervisely JSON

images and annotations into the upload space.

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

YOLOv5 PyTorch TXT

Congratulations, you have successfully converted your dataset from

Supervisely JSON

format to

YOLOv5 PyTorch TXT

format!

Next Steps

Ready to use your new

YOLOv5

dataset?

Now you probably want to use your new annotations with our YOLO v5 tutorial to get a model working with your own dataset.

Here are some compatible models:

YOLOv5

A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.