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Add FAQ sections to Modes and Tasks (#14181)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
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@ -241,57 +241,68 @@ The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12
## FAQ
### What is FastSAM and how does it work?
### What is FastSAM and how does it differ from SAM?
FastSAM, or Fast Segment Anything Model, is a real-time CNN-based solution designed to segment any object within an image. It decouples the segmentation task into two stages: all-instance segmentation and prompt-guided selection. The first stage uses [YOLOv8-seg](../tasks/segment.md) to produce segmentation masks for all instances in the image. The second stage outputs the region-of-interest based on user prompts. This approach significantly reduces computational demands while maintaining competitive performance, making it ideal for various vision tasks.
FastSAM, short for Fast Segment Anything Model, is a real-time convolutional neural network (CNN)-based solution designed to reduce computational demands while maintaining high performance in object segmentation tasks. Unlike the Segment Anything Model (SAM), which uses a heavier Transformer-based architecture, FastSAM leverages [Ultralytics YOLOv8-seg](../tasks/segment.md) for efficient instance segmentation in two stages: all-instance segmentation followed by prompt-guided selection.
### How does FastSAM compare to the Segment Anything Model (SAM)?
### How does FastSAM achieve real-time segmentation performance?
FastSAM addresses the limitations of SAM, which is a heavy Transformer model requiring substantial computational resources. FastSAM offers similar performance with significantly reduced computational demands by leveraging CNNs for real-time segmentation. It achieves competitive results on benchmarks like MS COCO with faster inference speeds using a single NVIDIA RTX 3090. This makes FastSAM a more efficient and practical solution for real-time industrial applications.
FastSAM achieves real-time segmentation by decoupling the segmentation task into all-instance segmentation with YOLOv8-seg and prompt-guided selection stages. By utilizing the computational efficiency of CNNs, FastSAM offers significant reductions in computational and resource demands while maintaining competitive performance. This dual-stage approach enables FastSAM to deliver fast and efficient segmentation suitable for applications requiring quick results.
### Can I use FastSAM for real-time segmentation and what are its practical applications?
### What are the practical applications of FastSAM?
Yes, FastSAM is designed for real-time segmentation tasks. Its efficiency and reduced computational demands make it suitable for various practical applications, including:
FastSAM is practical for a variety of computer vision tasks that require real-time segmentation performance. Applications include:
- Industrial automation where quick segmentation results are necessary.
- Real-time tracking in video streams ([tracking mode](../modes/track.md)).
- Real-time object detection and segmentation in autonomous systems.
- Security and surveillance systems requiring prompt object segmentation.
- Industrial automation for quality control and assurance
- Real-time video analysis for security and surveillance
- Autonomous vehicles for object detection and segmentation
- Medical imaging for precise and quick segmentation tasks
### How do I use FastSAM for inference in Python?
Its ability to handle various user interaction prompts makes FastSAM adaptable and flexible for diverse scenarios.
You can easily integrate FastSAM into your Python applications for inference. Here's an example:
### How do I use the FastSAM model for inference in Python?
To use FastSAM for inference in Python, you can follow the example below:
```python
from ultralytics import FastSAM
from ultralytics.models.fastsam import FastSAMPrompt
# Define an inference source
source = "path/to/image.jpg"
source = "path/to/bus.jpg"
# Create a FastSAM model
model = FastSAM("FastSAM-s.pt") # or FastSAM-x.pt
# Run inference on an image
results = model(source, device="cpu", retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
everything_results = model(source, device="cpu", retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
# Process the prompts
prompt_process = FastSAMPrompt(source, results, device="cpu")
annotations = prompt_process.everything_prompt()
prompt_process.plot(annotations=annotations, output="./")
# Prepare a Prompt Process object
prompt_process = FastSAMPrompt(source, everything_results, device="cpu")
# Everything prompt
ann = prompt_process.everything_prompt()
# Bounding box prompt
ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
# Text prompt
ann = prompt_process.text_prompt(text="a photo of a dog")
# Point prompt
ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
prompt_process.plot(annotations=ann, output="./")
```
This snippet demonstrates the simplicity of loading a pre-trained model and running predictions. For more details, refer to the [predict mode](../modes/predict.md).
For more details on inference methods, check the [Predict Usage](#predict-usage) section of the documentation.
### What are the key features of FastSAM?
### What types of prompts does FastSAM support for segmentation tasks?
FastSAM offers several key features:
FastSAM supports multiple prompt types for guiding the segmentation tasks:
1. **Real-time solution**: Leveraging CNNs for immediate results.
2. **Efficiency and performance**: Comparable to SAM but with reduced computational resources.
3. **Prompt-guided segmentation**: Flexibility to segment objects based on various user interactions.
4. **Based on YOLOv8-seg**: Utilizes YOLOv8's capabilities for instance segmentation.
5. **Benchmark performance**: High scores on MS COCO with faster inference speeds.
6. **Model compression feasibility**: Demonstrates significant reduction in computational effort while maintaining performance.
- **Everything Prompt**: Generates segmentation for all visible objects.
- **Bounding Box (BBox) Prompt**: Segments objects within a specified bounding box.
- **Text Prompt**: Uses a descriptive text to segment objects matching the description.
- **Point Prompt**: Segments objects near specific user-defined points.
These features make FastSAM a powerful tool for a wide array of vision tasks. For a comprehensive list of features, visit the [FastSAM overview](#overview).
This flexibility allows FastSAM to adapt to a wide range of user interaction scenarios, enhancing its utility across different applications. For more information on using these prompts, refer to the [Key Features](#key-features) section.