Labeling Tutorial

To increase the labeling efficiency, we utilize the online tool segment.ai

Step 1

Create your own account and login.

Step 2

You can create your own dataset or collaborate with others.

Collaboration

Ask Fidel to give you the access to the shared dataset. Current labels include 9 classes. You can change the classes to make it more reasonable. The object template example here

Create new dataset

Click New dataset , add descriptions , select the labeling type(bounding box, segmentation,...), add the object type.

Add samples to upload the images you want to annotate. Then start labeling.

Step 3

Adjust the super pixel size to a reasonable value.

Click the object, select the label and press space key to end annotation for this object. After this you can click next object and repeat the procedure.

Step 4

After labeling and reviewing, export the dataset. official document here

pip install segments-ai

# change corresponding client information and output data type in coco_output.
python coco_output.py