Description:
Discover the incredible potential of Meta AI's Segment Anything Model (SAM) in this comprehensive tutorial! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often competitive with or even superior to prior fully supervised results.

🔍 Explore this in-depth guide as we walk you through setting up your Python environment, loading SAM, generating segmentation masks, and much more. Master the art of converting object detection datasets into segmentation masks and learn how to leverage this powerful tool for your projects.

Chapters:
00:00 - Introduction and Overview of SAM by Meta AI
01:00 - Setting up Your Python Environment
02:46 - Loading the Segment Anything Model
03:09 - Automated Mask Generation with SAM
06:36 - Generate Segmentation Mask with Bounding Box
10:02 - Convert Object Detection Dataset into Segmentation Masks
12:01 - Outro

Resources:

🌏 Roboflow: [ Ссылка ]
🌌 Roboflow Universe: [ Ссылка ]
✏️ Roboflow Annotate power by SAM: [ Ссылка ]

📚 How to Use the Segment Anything Model (SAM) blog post: [ Ссылка ]
📓 How to Segment Anything with SAM notebook: [ Ссылка ]
🎬 Segment Anything Model (SAM) Breakdown YouTube video: [ Ссылка ]
🔥 Automated Data Labeling with SAM: [ Ссылка ]

💻 Segment Anything Model repository: [ Ссылка ]
🌌 Access the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images: [ Ссылка ]
📚 Segment Anything arXiv paper: [ Ссылка ]

On July 29th, 2024, Meta AI released Segment Anything 2 (SAM 2), a new image and video segmentation foundation model. According to Meta, SAM 2 is 6x more accurate than the original SAM model at image segmentation tasks. Learn more: [ Ссылка ]

Stay updated with the projects I'm working on at [ Ссылка ] and [ Ссылка ]! ⭐

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#MetaAI #SegmentAnythingModel #SAM #ImageSegmentation #Python #FoundationModels #ComputerVision #ZeroShot

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