You’ve been sitting at your desk for the last 3 hours, straining to zoom in and out to label all the classes on the image, and you’ve got 2,354 images to go. You start to wonder, isn’t there a better way to do this?
Naturally, you Google “quick way to annotate many images”, or “how to automate image labeling”. Some companies offer an annotation tool, which doesn’t solve your problem because you still have to annotate the images yourself. You then discovered annotation services, where you’ll get a workforce to annotate your data. However, you realised engaging these companies is a long-winded process where you need to sit through sales calls, demos, and contract negotiations.
You also come across some data labeling platforms that have the annotation tool and workforce component, but then engaging the workforce doesn’t seem straightforward. You wonder why you have to work with multiple companies just to annotate some images.
Why can’t there be a solution where a labeling platform already has a workforce plugged-in, so you can use it manage your data labeling projects without labeling the data yourself?
We asked the same question and that’s why we built SUPA BOLT, allowing anyone to scale data labeling operations within 24 hours.
This is how it can be done.
As you can see from the steps above, within 3-5 hours, you’re able to get back your first iteration of annotated data. After that, you’re able to scale up your labeling project with thousands of images and export the output in the next few hours.
This means within 24 hours, you’re able to start training your machine learning model with a large annotated dataset, without going through sales calls, demos, and other hassles.
Try BOLT yourself to experience the quick turnaround and train your machine learning in no time, without labeling a single image yourself.
Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.
I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.