Meta's 'Segment Anything' Model: Image Segmentation Overview

In this computer vision tutorial video, we'll explore SAM, the groundbreaking image segmentation model that can segment ANY image with astounding accuracy.

SAM, short for Segment Anything Model, is a powerful foundation model developed by Meta. It has been trained on an extensive dataset called Segment Anything, which consists of over 11 million images containing more than 1 billion meticulously annotated masks.

We're excited to announce that both the Segment Anything dataset and the SAM model have been open sourced, offering endless possibilities for applications like Augmented Reality Glasses and Bio-Medical Image Segmentation.

The creation of the Segment Anything Dataset was a meticulous process involving three stages. First, we had the Assisted-Manual Stage, where annotators interactively labeled the data using a pretrained SAM model. Next, we moved to the Semi-Automatic Stage, where SAM took charge of most of the annotation work while human annotators focused on less prominent objects. Finally, we reached the Fully Automatic Stage, where SAM autonomously annotated the remaining data.

The result of this comprehensive effort is the SA-1B dataset, boasting an incredible 1 billion masks across 11 million images. Compared to OpenImages V5, this dataset contains six times more images and a staggering 400 times more masks.

One of the key advantages of SAM is its promptable design, enabling it to segment objects in images regardless of their classes. Whether you're dealing with people, animals, objects, or anything in between, SAM is up to the task.

Join us in this video as we delve into the world of image segmentation with SAM, and discover the unlimited potential it holds.

Topics Covered:

✅What is the Segment Anything Project?
✅The Segment Anything Model (SAM)
✅The Segment Anything Dataset
✅What Can SAM Do?
✅Models Available Under the Open Source Project
✅Inference using SAM

Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.

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⭐️ Time Stamps:⭐️
00:00-00:11: Introduction
00:11-00:50: Segment Anything
00:50-01:34: Segment Anything Dataset
01:34-02:06: Segment Anything Model
02:06-02:27: SAM Pre-trained Weights
02:27-02:52: SAM in Action
02:52-03:39: Running Inference
03:39-04:31: Loading the Model
04:31-05:40: How the SAM Model works
05:40-06:01: Outro

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[alexiscureno]

Thank you so much for this video! This explains everything very detailed and cristal clear. Is it posible to use this same example but for live footage? example: simple webcam? TIA

[shamukshi]

for "solar panel counting from UAV image"...which approach is better ? 1. creating bounding box (BB) for solar panel using object detection model and then using BB as input for SAM....or.... 2. segmenting everything in the image from SAM...and then classifying each segment as solar panel and non solar panel.

[leixun]

Hi Thanks for sharing this video. I wonder if SAM generates different mask colours for different classes? Does it mean we can also use the colour for classification?

[srinisbir]

Where can i get the notebook that was used for this video?