SuperAnnotate has integrated and refined Meta AI’s Segment Anything Model (SAM), providing an advanced environment for fully experiencing the SAM to get higher quality training data and faster annotation with more scalability.
This webinar addresses the discovered bottlenecks of the SAM annotation tool, gives a detailed overview of the current SAM paper, and demonstrates how SuperAnnotate improved and fixed the limitations found in the original Meta AI annotation tool.
Segmentation tasks
First things first, it’s important to understand the different types of segmentation tasks:
- Instance segmentation assigns a unique label to each object.
- Semantic segmentation assigns a label to all the pixels.
- Panoptic segmentation assigns a unique label to all the pixels.
- Panoptic segmentation assigns a unique label to all the pixels, even if the objects belong to the same class.
Use cases
- Defect detection in infrastructure and manufacturing
- Instance segmentation objects in e-commerce and retail
- Area segmentation for remote sensing in forestry, agriculture, and insurance
- Autonomous vehicles
Segmentation methods
There are generally three different types of labeling approaches: manual segmentation, AI-assisted segmentation, and model-assisted segmentation.
With manual segmentation, we know that human beings are extremely precise. This method covers all use cases. However, manual annotation tools are slow.
AI-assisted segmentation speeds up the process significantly as it combines the strengths of human labeling and machine learning models. There are several AI-assisted labeling approaches: scribble-based, superpixel-based, points-based, edge points-based, and boxed-based. It’s important to note that AI-assisted labeling is not as accurate as we want it to be, and it doesn’t cover all use cases.
With model-assisted segmentation, humans are required to correct the model’s output. The better the model, the faster and more accurate the results are. This is where Meta’s Segment Anything Model comes into play.
Segment Anything Model
Segment Anything Model (SAM) is an AI model for computer vision applications by Meta that can segment (or cut out) any object from an image just by clicking on it. This revolutionizes segmentation tasks, significantly cutting down annotation time.
How did SuperAnnotate improve the SAM model?
SuperAnnotate’s machine learning team discovered bottlenecks, which they solved on the SuperAnnotate platform:
- Higher-quality annotations: using proprietary smart initialization techniques to deliver polygons that have higher quality.
- Faster annotation: scribble-based and superpixel-based approaches provide faster annotation than the initial point or box-based approach of Meta AI’s annotation tool.
- No latency issues: decreasing the inference and mask generation time of the algorithm for smoother annotation on high-resolution images.
Ready to power your innovation with AI? Then watch the webinar below.
