Labeling queue
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In object detection ML projects, labeling is the process of defining regions of interest in the frame.
Manually labeling images can become tedious and time-consuming, especially when dealing with huge datasets. This is why Edge Impulse studio provides an AI-assisted labeling tool to help you in your labeling workflows.
To use the labeling queue, you will need to set your Edge Impulse project as an "object detection" project. The labeling queue will only display the images that have not been labeled.
Currently, it only works to define bounding boxes (ingestion format used to train both MobileNetv2 SSD and FOMO models).
The labeling queue supports four different operation modes:
Using YOLOv5.
Using your current impulse.
Using any pretrained object detection 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.
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!
This only works with object detection models outputting bounding boxes. Centroid-based models (such as FOMO) won't work.
To label using a pretrained objection model:
Create a new (second) Edge Impulse project.
Choose Upload your model.
Select your model file (e.g. in ONNX or TFLite format), tell a bit about your model, and verify that the model gives correct suggestions via "Check model behavior".
Click Save model.
While still in this (second) project:
Go to Data acquisition and upload your unlabeled dataset.
Click Labeling queue, and under 'Label suggestions' choose "Classify using 'your project name'". You now get suggestions based on your uploaded model:
When you're done labeling, go to Data acquisition > Export data and export your (now labeled) dataset.
Import the labeled dataset into your original project.
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!
If you already have a labeled dataset containing bounding boxes, you can use the to import your data.
