Interviews

Coming soon

Guides

Coming soon

Market insights

Coming soon

Search

Personalize

Segment Anything by Meta AI

Segment Anything by Meta AI

Sam: segment anything, anytime, anywhere

Revolutionize image segmentation with SAM's AI precision. Ideal for labeling, inpainting, and data generation.
#75 in "Images
#81 in "Design
Price: Free

Desktop

Visit website
0%
Overview
Use cases
FAQ
Pricing & Discounts
UX/UI review
Team
Founder Interview
Funding
Overview
Use cases
FAQ
Pricing & Discounts
UX/UI review
Team
Founder Interview
Funding

Overview

The Segment Anything Model (SAM) developed by Meta AI represents a significant advancement in the field of computer vision, particularly in the area of image segmentation. SAM is designed to perform complex image segmentation tasks with a high degree of precision and versatility, setting new standards in this domain.

One of the key strengths of SAM is its integration of various technologies such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). This combination allows SAM to effectively understand a wide range of visual inputs and produce accurate segmentations. Additionally, SAM employs CLIP (Contrastive Language-Image Pre-training), developed by OpenAI, to enhance its capability to interpret text prompts in relation to images, making it highly adaptable to different tasks.

SAM's architecture is centered around three main components: a promptable segmentation task for zero-shot generalization, an innovative model design, and a comprehensive dataset. The prompt in SAM's context can be various forms of input, such as points, text, or boxes, indicating the areas to be segmented in an image. This flexibility is a key feature of SAM, allowing it to be applied in a wide range of scenarios.

The model's architecture includes an image encoder for generating image embeddings, a prompt encoder for processing the segmentation prompts, and a lightweight mask decoder that combines these embeddings to produce segmentation masks. This structure enables SAM to efficiently process and interpret both image and prompt data for accurate segmentation.

SAM's training relies on the SA-1B Dataset, which stands out as one of the most extensive and diverse image segmentation datasets available. It contains over 1 billion high-quality segmentation masks derived from around 11 million images. This vast dataset has been instrumental in training SAM, allowing it to handle an unprecedented range of image segmentation tasks with high accuracy.

The applications of SAM are broad and impactful. It can be used in AI-assisted labeling, helping to streamline the process of labeling images by automatically identifying and segmenting objects. In healthcare, SAM's precise segmentation abilities are beneficial for identifying specific regions for drug delivery, improving treatment precision. Additionally, SAM is suitable for land cover mapping, aiding in urban planning, agriculture, and environmental monitoring.

Looking to the future, SAM has the potential to significantly impact various sectors, including environmental monitoring, retail, autonomous vehicles, real-time surveillance, and even the entertainment industry. Its adaptability and precision make it a promising tool for a wide array of applications.

In terms of accessibility, SAM and its dataset, SA-1B, have been made open source for research purposes, which is a significant step towards democratizing advanced AI technologies in the field of image segmentation.

Use cases

The Segment Anything Model (SAM) by Meta AI, with its advanced capabilities in image segmentation, offers a range of practical applications across various fields. Here are some key use cases for SAM:

  1. Assisted Image Labeling: SAM can significantly enhance the process of image labeling. It automates the identification and segmentation of objects within images, which reduces the manual effort required for creating detailed annotations. This feature is particularly useful in computer vision tasks where precise labeling is crucial.
  2. Zero-Shot Labeling: SAM is capable of annotating images containing previously unseen objects. For instance, if SAM is provided with images containing cars, it can recommend segmentation masks for all the cars and other elements in the image. However, it's important to note that while SAM can segment images, it doesn't identify objects by their names, which might require additional processing through object detection models.
  3. Removing Backgrounds: SAM excels in distinguishing and segmenting backgrounds in images. This ability is invaluable in photo editing, where users might want to change or remove the background of an image. SAM can interactively select masks for the background, enabling the user to replace it with a transparent or different background.
  4. Inpainting: The accuracy of SAM in identifying object boundaries makes it an excellent tool for inpainting in image generation. This can be used to modify specific features of an image, like changing the color of objects, by identifying them with SAM and then processing the masks through an inpainting model.
  5. Synthetic Data Generation: By combining SAM with object detection models, it's possible to create synthetic data. This involves using segmentation masks to place objects on different backgrounds, which is useful for training models in environments representative of their deployment scenarios.

SAM's architecture leverages the synergy between CNNs (Convolutional Neural Networks) and GANs (Generative Adversarial Networks) to achieve these advanced segmentation capabilities. It also integrates technologies like CLIP (Contrastive Language-Image Pre-training) to process and respond to text-based inputs, enhancing its versatility.

Moreover, SAM is trained on the SA-1B Dataset, one of the most extensive and diverse image segmentation datasets available, containing over a billion high-quality segmentation masks. This vast dataset enables SAM to handle a wide range of image segmentation tasks with high accuracy and adapt to new segmentation challenges.

In terms of limitations, while SAM is highly capable in general segmentation tasks, it may not capture extremely fine details or small objects as effectively as some specialized models. This is due to its architecture, which focuses more on coverage and generalization than on the granularity of details.

These use cases and capabilities of SAM demonstrate its potential in revolutionizing image segmentation applications across various industries, from healthcare and urban planning to environmental monitoring and more.

FAQ

SAM is an advanced image segmentation model developed by Meta AI. It's designed to identify the precise location of objects in an image, whether specific or general, with high precision.

SAM uses a combination of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to perform image segmentation tasks. It also integrates CLIP (Contrastive Language-Image Pre-training) to process text-based inputs, making it versatile in understanding and segmenting images based on both visual and textual cues.

The SA-1B Dataset is a massive collection of over 1 billion high-quality segmentation masks derived from around 11 million images. This dataset, which covers a broad spectrum of scenarios and environments, is pivotal in training SAM for a wide range of segmentation tasks.

SAM can be used in various fields including AI-assisted labeling, medicine delivery, and land cover mapping. Its precision in segmentation makes it valuable in healthcare, urban planning, agriculture, and environmental monitoring. Additionally, SAM's potential extends to fields like retail, autonomous vehicles, real-time surveillance, and augmented reality.

Yes, SAM is open source and released under an Apache 2.0 license, making it accessible for widespread use in the community.

SAM can be integrated into projects by setting up a Python environment and utilizing its capabilities to generate masks automatically or create segmentation masks using bounding boxes. Tools like Roboflow Annotate use SAM for automated polygon labeling in browser-based applications.

While SAM is highly effective in general segmentation tasks, it might not capture extremely fine details or small objects as effectively as some specialized models. This is due to its focus on coverage and generalization over the granularity of details.

SAM is expected to evolve with better real-time processing capabilities, making it a promising tool for applications requiring immediate segmentation results.

Pricing & Discounts

The Segment Anything Model (SAM) by Meta AI is available for use at no cost. This means that users can access and utilize the tool's features and capabilities without a subscription fee. As a free tool, SAM offers the opportunity for businesses, researchers, and developers to leverage its advanced image segmentation capabilities without financial barriers. However, for the most accurate and current details about the tool, including any potential updates or changes to its availability and usage terms, it's advisable to refer directly to Meta AI's official resources or contact them for information.

Team

Meta, the company behind the development of Segment Anything, has a significant team and leadership structure. The company boasts 1,754 current employee profiles, with key figures including Founder, Chairman, and CEO Mark Zuckerberg. In addition to its employees, Meta has a board comprising 25 members and advisors, one of whom is Peter Thiel. This extensive team, with its diverse range of roles and expertise, plays a crucial role in driving Meta's various projects, including advanced itiatives like Segment Anything.

person

Mark Zuckerberg

Founder, Chairman, and CEO

person

Peter Thiel

Advisor

Funding

Meta, previously known as Facebook, has a significant funding history that has contributed to its development and expansion, including projects like Segment Anything. According to information from Crunchbase, Meta has raised a total of $24.6 billion in funding over 14 rounds. Their latest funding was raised on August 4, 2022, from a Post-IPO Debt round.

Some key points about Meta's funding are:

  • Total Funding Amount: $24.6 billion over 14 rounds.
  • Latest Funding Type: Post-IPO Debt round.
  • Notable Investors: Meta is supported by 25 investors, with PayPal and All Blue Capital being the most recent.
  • Investment Activities: Meta has made 54 investments, with their most recent one in Tyndall National Institute, which raised €5M on June 22, 2023.
  • Diversity Investments: Meta has made 6 diversity investments, including a recent one in Wisecut, which raised $50K on April 6, 2023.
  • Acquisitions: Meta has acquired 101 organizations, with the most recent being Gary Sharp Innovations on January 12, 2023.
  • Stock Information: Meta is registered under the ticker NASDAQ:META. Their stock opened at $38.00 in their May 18, 2012, IPO.

This funding history reflects Meta's robust financial backing and its active role in investment and acquisition within the tech industry. These financial resources have enabled Meta to pursue ambitious projects like Segment Anything and to continue innovating in the field of artificial intelligence and beyond.

Alina  Chernomorets

Published by: Alina Chernomorets

12 September 2023, 12:00AM

TAKE QUIZ TO GET

RELEVANT CONTENT

Blue robot
Brown robot
Green robot
Purple robot

Share this material in socials

Copy link

Join our newsletter

Stay in the know on the latest alpha, news and product updates.