METAs Segment Anything Model (SAM) Complete Breakdown

As an AI researcher who has dedicated over four years to working with segmentation models, I fully understand the challenges of obtaining properly annotated datasets. The task of segmenting a new class of objects is daunting, often requiring the painstaking collection and annotation of thousands of images. While drawing bounding boxes for object detection is relatively straightforward, achieving pixel-perfect predictions demands way more effort.

Even with state-of-the-art annotation tools, the complexity of annotating complex images limits human annotators to a mere 20 images per hour.

META’s Segment Anything Model (SAM) presents a groundbreaking method to significantly accelerate the annotation for a vast array of objects. So, without further ado, let’s look into the details of this awesome model.

Topics Covered

• What Is Segment Anything Model or SAM?
• Why Do We Need SAM?
• SAMs Architecture
• SAM Model Training
• Results
• What’s New In SAM 2?

Different images with different numbers of masks (Img Src)

What Is Segment Anything Model or SAM?

SAM is a state-of-the-art AI model developed by Meta AI that can identify and segment any…