One of the core pillars of all Hasty is AI-powered annotation. With Hasty focusing on the vision AI field, it brings various best-in-class labeling automation tools for many Computer Vision tasks. One of these options is the Instance Segmentation AI assistant.
Instance Segmentation AI assistant (IS AI assistant) focuses on the comprehensive annotation automation for an Instance Segmentation task. This means that the assistant automates:
Finding instances of the target classes;
Creating segmentation masks of the correct shapes;
And labeling the masks.
Such an approach offers an exponentially quicker way of preparing data for Instance Segmentation at scale.
To select the assistant, please navigate to the Annotation Environment and press the following icon:
or you can use a hotkey and simply press “I” while in the Annotation environment.
When selected, the assistant will produce annotation suggestions that will look like segmentation mask shapes with a dotted border.
Please notice that these shapes can have different colors. The colors are the same as in your label classes but more transparent. So, the assistant's output can be interpreted as the potential shape of each instance the model has found in your image and the label class for each object.
As a user, you only have to accept the algorithm’s suggestion if you think they are good enough. You can do this by left-clicking on the shape you want to approve.
You can also accept all suggestions by pressing "Enter".
If you do not see any shapes or they are not covering the objects as you want, please try adjusting the model’s confidence modifier.
Check out our Getting Start with Hasty tutorial to see the assistants in practice before starting a new project.
Hasty also has a Youtube channel featuring video tutorials for each vision AI task. Here is the one about the Instance Segmentation task in general and the IS AI assistant in particular.
The first results from our assistants might underwhelm you. This is perfectly normal. What makes Hasty different is that our AI assistants improve the more data they see. After 10 images, you might have a 10% assistance rate (percentage of annotations created by assistants). This is fine. When you've annotated 100 images, it might be 40-65%. When you have thousands of pictures annotated, we can offer remarkably high percentages of automation - everything from 98% to 92% - depending on the use case.
The models retrain after having seen 20% more data. That means a new training is initialized after 12, 15, 19 images, etc.
This modifier controls which potential segmentation masks are shown. The higher the confidence value, the fewer potential shapes you see, but those shapes are the ones that our model is the most confident in. The modifier can be changed by adjusting the value in the tool settings bar or by using the hotkeys “,” and “.”.