Segment Anything Model (SAM): An introduction скачать в хорошем качестве

Segment Anything Model (SAM): An introduction

For pro access, check this: https://vizuara.ai/courses/transforme... In this lecture, we take a deep and very structured walk through the Segment Anything Model (SAM) introduced by Meta, starting from the very basics of what image segmentation actually means and slowly building up to the full architecture of SAM, including why it is fundamentally different from traditional segmentation models like Mask R-CNN and why it is now considered a foundational model for vision. We begin by clearly distinguishing segmentation from object detection and classification, explaining why pixel-level understanding is a much harder and more meaningful problem, and how semantic segmentation and instance segmentation differ in practice. From there, we move into the core motivation behind Segment Anything, which is the idea of promptable segmentation, where the model does not just passively segment predefined classes but instead responds to user intent through prompts such as clicks, bounding boxes, rough masks, and even text-based descriptions. A major part of this lecture is dedicated to understanding the different types of prompts supported by SAM, including point prompts, box prompts, mask prompts, and how text prompts are indirectly supported using CLIP embeddings. We carefully discuss why prompts are inherently ambiguous, how SAM handles this ambiguity by producing multiple mask hypotheses in a single forward pass, and how this design choice makes the model extremely practical for real-world interactive applications. We then break down the complete SAM architecture into its three core components: the image encoder, the prompt encoder, and the mask decoder, drawing parallels with Detection Transformers to make the design intuition very clear. You will understand where self-attention is used, where cross-attention appears, why a decoder is necessary for mask generation, and how initialized mask tokens play a role similar to object queries in DETR. An important section of the lecture focuses on why SAM inference is so fast in practice, despite using a heavy Vision Transformer backbone. We explain how image embeddings are computed once and reused across multiple prompt queries, which is the key reason SAM can support real-time interactive segmentation tools. Finally, we also discuss how the massive SAM dataset was created in stages, starting from human-annotated masks and gradually moving towards large-scale semi-automated and automated mask generation, resulting in over a billion masks across millions of images. This gives you a strong appreciation of why SAM generalizes so well across domains and object categories. This lecture is ideal for students, researchers, and practitioners who want a first-principles understanding of Segment Anything, especially if you are working on computer vision, vision transformers, multimodal models, or interactive AI systems.