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ultralytics 8.0.123 Ubuntu security and VideoWriter codec fixes (#3380)

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Glenn Jocher
2023-06-25 23:59:28 +02:00
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docs/models/fast-sam.md
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@ -12,7 +12,7 @@ The Fast Segment Anything Model (FastSAM) is a novel, real-time CNN-based soluti
## Overview
FastSAM is designed to address the limitations of the Segment Anything Model (SAM), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses a Convolutional Neural Network (CNN)-based detector to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
FastSAM is designed to address the limitations of the [Segment Anything Model (SAM)](sam.md), a heavy Transformer model with substantial computational resource requirements. The FastSAM decouples the segment anything task into two sequential stages: all-instance segmentation and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce the segmentation masks of all instances in the image. In the second stage, it outputs the region-of-interest corresponding to the prompt.
## Key Features
@ -22,9 +22,9 @@ FastSAM is designed to address the limitations of the Segment Anything Model (SA
3. **Prompt-guided Segmentation:** FastSAM can segment any object within an image guided by various possible user interaction prompts, providing flexibility and adaptability in different scenarios.
4. **Based on YOLOv8-seg:** FastSAM is based on YOLOv8-seg, an object detector equipped with an instance segmentation branch. This allows it to effectively produce the segmentation masks of all instances in an image.
4. **Based on YOLOv8-seg:** FastSAM is based on [YOLOv8-seg](../tasks/segment.md), an object detector equipped with an instance segmentation branch. This allows it to effectively produce the segmentation masks of all instances in an image.
5. **Competitive Results on Benchmarks:** On the object proposal task on MS COCO, FastSAM achieves high scores at a significantly faster speed than SAM on a single NVIDIA RTX 3090, demonstrating its efficiency and capability.
5. **Competitive Results on Benchmarks:** On the object proposal task on MS COCO, FastSAM achieves high scores at a significantly faster speed than [SAM](sam.md) on a single NVIDIA RTX 3090, demonstrating its efficiency and capability.
6. **Practical Applications:** The proposed approach provides a new, practical solution for a large number of vision tasks at a really high speed, tens or hundreds of times faster than current methods.
@ -32,7 +32,7 @@ FastSAM is designed to address the limitations of the Segment Anything Model (SA
## Usage
FastSAM is not yet available directly via the `ultralytics` package, but it is available directly from the [https://github.com/CASIA-IVA-Lab/FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) repository. Here is a brief overview of the typical steps you might take to use FastSAM:
FastSAM is not yet available within the [`ultralytics` package](../quickstart.md), but it is available directly from the [https://github.com/CASIA-IVA-Lab/FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) repository. Here is a brief overview of the typical steps you might take to use FastSAM:
### Installation