As an annotator, it’s all about pixel precision. In fact, data preparation and engineering tasks represent over 80% of the time consumed in most AI and Machine Learning projects, according to Cognilytica. When it comes to semantic segmentation you strive for pixel-perfect accuracy, and this can become quite a complicated task. For example, with occluded objects. If you want a fine-tuned “pixel-perfect” annotation, you’re going to have to put your fine-tuning gloves on. That’s where Dataloop comes in.
Gain 100% Pixel Enforcement with Dataloop
That’s right, we’re bringing semantic segmentation to a whole new level. It’s easy to miss pixels, and I’m sure you can relate to the frustration of missing those few isolated pixels. Even if you’re an annotation manager, ensuring your annotators are producing quality data is of utmost concern, you can’t have true data quality unless your datasets are complete and accurate. The more labels you’re talking about, the more difficult this task becomes, this is due to the fact that it’s difficult to see the missing pixels when the image is loaded with annotations and layers. If an annotator misses a few pixels, it puts your model and data quality in jeopardy, because we know that a model is only as good as its data. If you’re missing pixels, you’re not going to get the desired results. Additionally, missing pixels will result in an error, and you’ll be unable to move on, and will be required to go back to the annotation stage.
As it is, the annotation process is heavily time-consuming, in fact, according to researchers at Google, they calculated that annotating an image for the COCO dataset takes an average of 19 minutes, and fully annotating a single image from the Cityscapes datasets takes 1.5 hours. This isn’t even referring to advanced segmentation methods. Therefore, when considering the amount of time it takes to annotate, every second matters.
With Dataloop, it doesn’t have to be this way. In the Dataloop’s labeling process, the project manager can request 100% pixel enforcement. This option will find the missing pixels for you. This way you’ll know what you’ve missed, and can label accordingly – keeping that high-level quality as you scale. There’s even a highlight option that will highlight the missing pixels as well as a hotkey to highlight these missing pixels.
Select the pixel enforcement option in the recipe, and voilà, in one click the pixel enforcement feature highlights the missing pixels for you, you can make the fixes, and your work is complete, and you can move on.
How Pixel Enforcement Gives You 4 Quick Wins:
✅ More accurate annotations: The more complete the annotation the better results/quality. When the image is fully annotated there’s no gaps that need to be interpreted, and therefore more precise.
✅ Avoid unnecessary loops: You’re able to finish annotating in one round.
✅ Accurate models: Pixel enforcement helps you identify the missing pixels and with a complete and more accurate annotation this ensures your model is better.
✅ Faster work process: Manually searching for the missing pixels or running a script is still time-consuming, with pixel enforcement you can cut out all that wasted time.
Ready to save yourself or your annotation team the time and frustration and make the annotation process easier? If you’d like to hear more about pixel enforcement or any other feature – feel free to check our resource library, or set up a time with one of our product consultants who’d be happy to discuss further.
