Labeling Queue (Images)
Last updated
Last updated
If you are working on an object detection project, you will most likely see "labelling queue" bar on your data acquisition page. The labeling queue shows you all the data that has not been labelled in your dataset.
Can't see the labeling queue? Go to Dashboard, and under 'Project info > Labeling method' select 'Bounding boxes (object detection)'.
In object detection, labelling is the process of adding a bounding box around specific objects in an image so that your machine learning model can learn and infer from it. Edge impulse studio has an inbuilt data annotation tool with AI assisted labelling to assist you in your labelling workflows as we will see.
In the Edge Impulse studio, labelling your data is as easy as dragging a box around the object, then entering a label and saving as shown below.
However, as simple the manual labelling process might look like, it sometimes can become tedious and time consuming especially when dealing with huge datasets. To make your life easier, Edge Impulse studio has an inbuilt AI-assisted labelling feature to automatically assist you in your labelling workflows.
there are 3 ways you can use to perform AI assisted labelling on the Edge Impulse Studio:
Using yolov5
Using your own model
Using object tracking
By utilizing an existing library of pre-trained object detection models from YOLOv5 (trained with the COCO dataset), common objects in your images can quickly be identified and labeled in seconds without needing to write any code!
To label your objects with YOLOv5 classification, click the Label suggestions dropdown and select “Classify using YOLOv5.” If your object is more specific than what is auto-labeled by YOLOv5, e.g. “coffee” instead of the generic “cup” class, you can modify the auto-labels to the left of your image. These modifications will automatically apply to future images in your labeling queue.
Click Save labels to move on to your next raw image, and see your fully labeled dataset ready for training in minutes!
You can also use your own trained model to predict and label your new images. From an existing (trained) Edge Impulse object detection project, upload new unlabeled images from the Data Acquisition tab. Then, from the “Labeling queue”, click the Label suggestions dropdown and select “Classify using ”:
You can also upload a few samples to a new object detection project, train a model, then upload more samples to the Data Acquisition tab and use the AI-Assisted Labeling feature for the rest of your dataset. Classifying using your own trained model is especially useful for objects that are not in YOLOv5, such as industrial objects, etc.
Click Save labels to move on to your next raw image, and see your fully labeled dataset ready for training in minutes using your own pre-trained model!
If you have objects that are a similar size or common between images, you can also track your objects between frames within the Edge Impulse Labeling Queue, reducing the amount of time needed to re-label and re-draw bounding boxes over your entire dataset.
Draw your bounding boxes and label your images, then, after clicking Save labels, the objects will be tracked from frame to frame:
Now that your object detection project contains a fully labeled dataset, learn how to train and deploy your model to your edge device: check out our tutorial!
We are excited to see what you build with the AI-Assisted Labeling feature in Edge Impulse, please post your project on our forum or tag us on social media, @Edge Impulse!
